Pith. sign in

REVIEW 4 major objections 5 minor 1 cited by

UltraLightSqueezeNet: A Deep Learning Architecture for Malaria Classification with up to 54x fewer trainable parameters for resource constrained devices

T0 review · 4 major / 5 minor · reviewed 2026-08-10 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read SqueezeNet1.1's four-fire-module variant reaches 96.55% malaria classification accuracy—within 0.57 points of the original—while cutting trainable parameters by sixfold.

desk verdict A clean but incremental fire-module ablation whose '6x compute saving' claim is actually a parameter-count ratio, not measured compute. read the letter →

arxiv 2501.14172 v2 pith:2MS45BWQ submitted 2025-01-24 cs.LG cs.AIcs.CV

Suresh Babu Nettur , Shanthi Karpurapu , Unnati Nettur , Likhit Sagar Gajja , Sravanthy Myneni , Akhil Dusi , Lalithya Posham This is my paper · ORCID
classification cs.LGcs.AIcs.CV MSC 68T0792C55
keywords malariaclassificationSqueezeNetlightweightCNNfiremoduleresource-constraineddevicesmedicalimagingbinarydeeplearning
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper proposes three ultra-lightweight versions of the SqueezeNet1.1 convolutional network for classifying malaria-infected versus healthy red blood cells, built by keeping only one, two, or four of the original architecture's eight fire modules. The goal is to see how far the parameter count can be cut before accuracy degrades, so that automated malaria screening can run on phones, microscopes, and other memory-limited hardware. On a standard malaria cell dataset, the four-fire-module variant reaches 96.55% accuracy with six times fewer trainable parameters than the 97.12% full model, and the one-fire-module variant cuts parameters 54-fold at a cost of about 2.4 accuracy points. The authors argue this gives a flexible accuracy-efficiency dial for resource-constrained deployment.

What carries the argument

The load-bearing component is the fire module, a two-layer block in which a squeeze layer uses 1x1 convolutions to compress the channel count and an expand layer mixes 1x1 and 3x3 convolutions to build features cheaply. The proposed variants are made by truncating SqueezeNet1.1 after its first one, two, or four fire modules and then attaching a 1x1 convolution, global average pooling, and softmax head. The number of fire modules is the single dial that trades parameter count, latency, and storage (from 13,458 to 723,522 trainable parameters) against classification accuracy.

What would settle it

Train each of the four architectures several times (e.g., five random seeds) on the same malaria dataset and validation split, then compare the spread of validation accuracies; if the 96.55% and 97.12% results are within one standard deviation of each other, the claimed near-lossless sixfold parameter reduction is not supported.

Watch

Extended reading notes

Core claim

The central claim is that the number of fire modules in SqueezeNet1.1 can be reduced dramatically without sacrificing practical diagnostic performance. In the reported experiments, the full eight-fire-module SqueezeNet1.1 reaches 97.12% accuracy and 0.9953 AUC, while the four-fire-module Variant 3 reaches 96.55% accuracy and 0.9925 AUC with 120,930 trainable parameters—about one-sixth of the original 723,522. The two- and one-fire-module variants drop to roughly 94.6–94.8% accuracy but reduce parameters by 28x and 54x respectively, with correspondingly shorter training and inference times. The paper interprets this as diminishing returns: each additional fire module improves accuracy, but the gains level off, so the smallest architecture that meets the target accuracy is the right choice for a given device.

Load-bearing premise

The comparison rests on a single training run per architecture, so the small accuracy gap of about half a point between the full model and the best variant could be no more than random variation.

Editorial extensions

If this is right

  • A four-fire-module SqueezeNet (about 121k parameters, roughly 0.46 MB) can replace the 723k-parameter original in memory-limited deployments with under a percentage point of accuracy loss.
  • On the 5,512-image validation set, the four-fire-module variant's AUC (0.9925) stays close to the original's (0.9953), indicating similar ability to separate infected from uninfected cells.
  • The one- and two-fire-module variants cut inference time by roughly 44–56% relative to the original while keeping accuracy above 94%, which may matter for extreme edge devices.
  • The pattern of diminishing returns suggests that adding fire modules beyond a certain point yields little accuracy gain for the added compute, so selecting the smallest variant that meets an accuracy threshold is a reasonable design rule.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Beyond the paper: because each architecture was trained only once, the exact ranking of Variant 1 versus Variant 2 (94.76% vs 94.59%) should be treated as noise until repeated-seed experiments are run.
  • Beyond the paper: the same fire-module truncation recipe could plausibly be applied to other squeeze-and-expand architectures for other medical imaging tasks, though the paper only tests malaria images and does not claim this generality.
  • Beyond the paper: the reported numbers come from a balanced public dataset; real-world malaria screening sees imbalanced prevalence and varied staining, so deployment-focused follow-ups should test these variants on imbalanced or out-of-distribution cell images.
  • Beyond the paper: an immediate testable extension is to swap the softmax head for a calibrated output and measure reliability curves, since the paper reports accuracy but not calibration.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 5 minor

Summary. The paper proposes three reduced-fire-module variants of SqueezeNet1.1 (Variant 1 with one, Variant 2 with two, Variant 3 with four fire modules) and trains them on the NIH/Kaggle malaria cell-image dataset using a single 80/20 train/validation split. It reports accuracy, precision, recall, F1, AUC, confusion matrices, training time, inference time, and model size for the variants and the original SqueezeNet1.1. The central claims are that SqueezeNet1.1 achieves the best accuracy (97.12%), Variant 3 is a near-equal substitute (96.55%) with a claimed 6x reduction in computational overhead, and Variants 1 and 2 offer 54x and 28x reductions in trainable parameters with lower but still usable accuracy.

Significance. If the efficiency claim were supported, the paper would provide a useful incremental data point for lightweight malaria classification at the edge. The authors correctly report internally consistent accuracy numbers: the confusion matrices in Figure 8 reproduce the accuracies in Table 2, and the parameter ratios for the variants are straightforward from the architecture definitions. However, the flagship '6x reduction in computational overhead' claim is not a measured quantity; it is the trainable-parameter ratio, and the paper's own timing data in Figure 10 shows far smaller reductions. The variants themselves are simple ablations of an existing architecture, so the contribution hinges entirely on whether the efficiency versus accuracy trade-off is demonstrated with credible measurements and repeated runs. As it stands, the paper is an honest but under-analyzed empirical report whose headline claim goes beyond the evidence presented.

major comments (4)
  1. [Abstract and Section VII (Conclusion)] The claim that Variant 3 provides a '6x reduction in computational overhead' is not supported by any direct measurement. The ratio 723,522 / 120,930 = 5.98 is a trainable-parameter ratio, not FLOPs, MACs, latency, or energy consumption. In the only timing evidence, Figure 10, Variant 3 reduces training time by only 6.5% and inference time by 22.2% relative to SqueezeNet1.1, while model size drops by 82.4%. Because early high-resolution 3x3 convolutions dominate compute in SqueezeNet-style designs, deleting later fire modules can reduce parameter count far more than compute. The abstract and conclusion should either be revised to say 'trainable parameters' instead of 'computational overhead,' or the authors should report FLOPs/MACs and latency on a deployed edge device.
  2. [Section IV, Tables 1-2 and Figure 8] All accuracy comparisons are based on a single training run per architecture. The differences that drive the paper's recommendation, such as 97.12% for SqueezeNet1.1 versus 96.55% for Variant 3 versus 94.76% for Variant 1, are presented without error bars, repeated seeds, or a statistical test. Single-run results on a balanced binary dataset with roughly 5,000 validation images can easily shift by a few tenths of a percent across seeds; the 'almost identical' conclusion for Variant 3 is therefore not yet established. The authors should report mean and standard deviation over at least five independent runs and, ideally, a paired comparison of the relevant model pairs.
  3. [Section IV, Figure 10 and experimental setup] The training and inference times were measured on Google Colab CPU resources, which is a shared and noisy environment, and no edge-device measurement is provided despite the title and introduction promising deployment for resource-constrained devices. Wall-clock CPU time in Colab is not a reliable proxy for inference cost on a Jetson, Raspberry Pi, or mobile phone, and it is especially insufficient to support a 6x computational-overhead claim. The authors should either replace these timings with controlled device-level measurements or restrict their efficiency claims to parameter counts and model size, which are already well established by the architecture definitions.
  4. [Section IV, Dataset and evaluation] The paper uses an 80/20 train/validation split and reports final metrics on the validation set; there is no separate test set. Because the authors then select Variant 3 as the best trade-off using these same validation metrics, the reported accuracy may be optimistically biased if any selection or early-stopping decisions were informed by validation performance. The paper should add a held-out test set not used in any decision, or explicitly state that no validation-based model selection was performed.
minor comments (5)
  1. [Throughout] The notation is inconsistent: the manuscript uses 'SqueezeNet1.1', 'SqueezeNetV1.1', 'SqueezeNet', and 'SquezeNet1.1' in different places; please standardize the spelling.
  2. [Section III, Variant 1 text after Table 1] The text says Variant 1 has a lower F1-score of 0.92 for the Parasitized class, but Table 1 reports 94.66% F1 for Parasitized and 94.85% for Uninfected; the narrative should be corrected to match the table.
  3. [Figure 6 and Figure 10] Figure 6 reports the original SqueezeNet1.1 model size as 2.76 MB, while Figure 10 reports 8.5 MB for the same model; the units, checkpoint format, or storage assumptions should be clarified so the two figures are consistent.
  4. [References] Reference [54] states that the GitHub repository 'will be made available post acceptance of the paper,' so the code is not currently available for reviewers or readers; if reproducibility is expected, an anonymous or public repository link should be provided at submission.
  5. [References and author affiliations] Several references are incomplete or are bare URLs (e.g., [1], [3], [4], [38]), and the affiliation numbering appears to have two items labeled '3'; these should be cleaned up before publication.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the paper reports held-out accuracy measurements and parameter counts that follow directly from architecture definitions; the efficiency claim is a proxy assumption, not a circular derivation.

full rationale

The paper's claim chain is empirical: SqueezeNet1.1 and three variants are defined by explicit fire-module counts, trained on the NIH/Kaggle malaria split, and evaluated on a fixed validation set of 5,512 images. Accuracy, precision, recall, F1, AUC, training/inference time, and model size are measured, not derived from a fitted parameter. The '54x/28x/6x fewer trainable parameters' statements are direct consequences of the stated architectures (13,458; 25,890; 120,930 vs 723,522 parameters), so they are construction-level counts rather than circular predictions. The only arguable issue is terminological: the conclusion calls the parameter ratio a '6x reduction in computational overhead' while Fig. 10 shows 6.5% training-time and 22.2% inference-time reductions. That is a validity or evidence concern, not a circularity concern, because the ratio is not used to generate the accuracy results. Self-citations [16]-[19] appear only in a background sentence about the authors' broader research objectives and are not load-bearing. The limitations section candidly restricts generalization to a single dataset and notes the lack of real-time assessment. No step reduces by construction to its inputs, so the circularity score is 0.

Assumptions & free parameters 2 free parameters · 2 assumptions · 0 invented entities

The paper introduces no new theoretical constructs; its contribution rests on standard CNN building blocks and a public dataset. The main hidden costs are the representativeness of the dataset and the assumption that parameter reductions translate into real device-level savings.

free parameters (2)
  • Variant configuration (number of fire modules) = 1, 2, 4, 8
    The central efficiency-accuracy comparison is between hand-picked architecture sizes; no search or theoretical principle selects these values, and the 'best' variant is chosen after seeing validation results.
  • Training hyperparameters (learning rate, batch size, epochs) = 1e-4, 32, 100
    These are standard choices reported in Section IV, but any change could alter the ranking of variants; they are not tuned per model.
assumptions (2)
  • domain assumption The Kaggle/NIH malaria dataset is representative of real-world blood smear images encountered in resource-constrained settings.
    The paper's claims about practical deployment depend on this; the authors themselves list this as a limitation in Section VI.
  • domain assumption Parameter count and CPU inference time are valid measures of computational overhead on edge devices.
    The '6x reduction in computational overhead' is mainly derived from model size and inference time on Google Colab CPU, not measured on mobile or embedded hardware.

how reviews work

0 comments
Cite this review

Pith. "Pith review of UltraLightSqueezeNet: A Deep Learning Architecture for Malaria Classification with up to 54x fewer trainable parameters for resource constrained devices." pith.science (2026). https://pith.science/paper/2MS45BWQ

@misc{pith2026250114172,
  author       = {Pith},
  title        = {Pith review of: UltraLightSqueezeNet: A Deep Learning Architecture for Malaria Classification with up to 54x fewer trainable parameters for resource constrained devices},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/2MS45BWQ}},
  note         = {Machine review of arXiv:2501.14172}
}
read the original abstract

Lightweight deep learning approaches for malaria detection have gained attention for their potential to enhance diagnostics in resource constrained environments. For our study, we selected SqueezeNet1.1 as it is one of the most popular lightweight architectures. SqueezeNet1.1 is a later version of SqueezeNet1.0 and is 2.4 times more computationally efficient than the original model. We proposed and implemented three ultra-lightweight architecture variants to SqueezeNet1.1 architecture, namely Variant 1 (one fire module), Variant 2 (two fire modules), and Variant 3 (four fire modules), which are even more compact than SqueezeNetV1.1 (eight fire modules). These models were implemented to evaluate the best performing variant that achieves superior computational efficiency without sacrificing accuracy in malaria blood cell classification. The models were trained and evaluated using the NIH Malaria dataset. We assessed each model's performance based on metrics including accuracy, recall, precision, F1-score, and Area Under the Curve (AUC). The results show that the SqueezeNet1.1 model achieves the highest performance across all metrics, with a classification accuracy of 97.12%. Variant 3 (four fire modules) offers a competitive alternative, delivering almost identical results (accuracy 96.55%) with a 6x reduction in computational overhead compared to SqueezeNet1.1. Variant 2 and Variant 1 perform slightly lower than Variant 3, with Variant 2 (two fire modules) reducing computational overhead by 28x, and Variant 1 (one fire module) achieving a 54x reduction in trainable parameters compared to SqueezeNet1.1. These findings demonstrate that our SqueezeNet1.1 architecture variants provide a flexible approach to malaria detection, enabling the selection of a variant that balances resource constraints and performance.

Figures

Figures reproduced from arXiv: 2501.14172 by the authors.

Figure 1
Figure 1. FIGURE 1 [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 2
Figure 2. FIGURE 2 [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. FIGURE 3 [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figures from the paper (6 more)
Figure 5
Figure 5. Figure 5: FIGURE 5 [PITH_FULL_IMAGE:figures/full_fig_p006_5.png]
Figure 6
Figure 6. Figure 6: FIGURE 6 [PITH_FULL_IMAGE:figures/full_fig_p007_6.png]
Figure 8
Figure 8. Figure 8: FIGURE 8 [PITH_FULL_IMAGE:figures/full_fig_p008_8.png]
Figure 7
Figure 7. Figure 7: FIGURE 7 [PITH_FULL_IMAGE:figures/full_fig_p008_7.png]
Figure 9
Figure 9. Figure 9: FIGURE 9 [PITH_FULL_IMAGE:figures/full_fig_p009_9.png]
Figure 10
Figure 10. Figure 10: FIGURE 10 [PITH_FULL_IMAGE:figures/full_fig_p010_10.png]

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Lightweight Weighted Average Ensemble Model for Pneumonia Detection in Chest X-Ray Images

    eess.IV 2025-01 reject novelty 3.0 of 10

    A weighted average of MobileNetV2 and NASNetMobile is reported to reach 98.63% accuracy on the Kermany pediatric pneumonia dataset, though the ensemble weights were optimized on the test set.

Reference graph

Works this paper leans on

54 extracted references · 52 canonical work pages · cited by 1 Pith paper

  1. [1]

    https://www.who.int/campaigns/world-malaria-day/2024

    work page 2024

  2. [2]

    Burden of malaria in Ethiopia, 2000– 2016: findings from the Global Health Estimates 2016

    Girum T., Shumbej T., Misgun S. Burden of malaria in Ethiopia, 2000– 2016: findings from the Global Health Estimates 2016. T ropical Diseases, Travel Medicine and Vaccines. 2019;5(1):p. 11. doi: 10.1186/s40794-019-0090-z

    work page doi:10.1186/s40794-019-0090-z 2000

  3. [3]

    https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6626392/

    work page

  4. [4]

    https://www.intechopen.com/chapters/86086

    work page

  5. [5]

    Deep learning- enabled medical comp uter vision

    Therine Chou, Serena Yeung, Nikhil Naik, Ali Madani, Ali Mottaghi, Yun Liu, Eric Topol, Jeff Dean, and Richard Socher. "Deep learning- enabled medical comp uter vision." NPJ digital medicine 4, no. 1 (2021): 5

    work page 2021

  6. [6]

    Computer-aided diagnosis based on extreme learning machine: a review

    Ang, Zhiqiong, Yiqi Luo, Junchang Xin, Hao Zhang, Luxuan Qu, Zhongyang Wang, Yudong Yao, Wancheng Zhu, and Xingwei Wang. "Computer-aided diagnosis based on extreme learning machine: a review." IEEE Access 8 (2020): 141657-141673

    work page 2020

  7. [8]

    A novel shallow convnet-18 for malaria parasite detection in thin blood smear images: Cnn based malaria parasite detection

    Elangovan, Poonguzhali, and Malaya Kumar Nath. "A novel shallow convnet-18 for malaria parasite detection in thin blood smear images: Cnn based malaria parasite detection." SN computer science 2, no. 5 (2021): 380

    work page 2021

  8. [9]

    A new approach for microscopic diagnosis of malaria parasites in thick blood smears using pre-trained deep learning models

    Nakasi, Rose, Ernest Mwebaze, Aminah Zawedde, Jeremy Tusubira, Benjamin Akera, and Gilbert Maiga. "A new approach for microscopic diagnosis of malaria parasites in thick blood smears using pre-trained deep learning models." SN Applied Sciences 2 (2020): 1-7

    work page 2020

Show all 54 references
  1. [10]

    Pre -trained deep convolutional neural network for detecting malaria on the human blood smear images

    Diyasa, I. Gede Susrama Mas, Akhmad Fauzi, Ariyono Setiawan, Moch Idhom, Radical Rakhman Wahid,javascript:void(0) and Alfath Daryl Alhajir . "Pre -trained deep convolutional neural network for detecting malaria on the human blood smear images." In 2021 International Conference...

    2021

  2. [11]

    Effective preprocessed thin blood smear images to improve malaria parasite detection using deep learning

    Swastika, W., G. M. Kristianti, and R. B. Widodo. "Effective preprocessed thin blood smear images to improve malaria parasite detection using deep learning." In Journal of Physics: Conference Series, vol. 1869, no. 1, p. 012092. IOP Publishing, 2021

    2021

  3. [12]

    Plasmodium Life Cycle-Stage Classification on Thick Blood Smear Microscopy Images using Deep Learning: A Contribution to Malaria Diagnosis

    Araujo, F. A. S. , N. D. Colares, U. P. Carvalho, C. F. F. Costa Filho, and M. G. F. Costa. "Plasmodium Life Cycle-Stage Classification on Thick Blood Smear Microscopy Images using Deep Learning: A Contribution to Malaria Diagnosis." In 2023 19th International Symposium on Med...

    2023

  4. [13]

    Texture analysis to detect malaria tropica in blood smears image using support vector machine

    Widodo, Sri. "Texture analysis to detect malaria tropica in blood smears image using support vector machine." International Journal of Innovative Research in Advanced Engineering 1, no. 8 (2014):

    2014

  5. [14]

    Detection of peripheral malarial parasites in blood smears using deep learning models

    Alharbi, Amal H., Meng Lin, B. Ashwini, Mohamed Yaseen Jabarulla, and Mohd Asif Shah. "Detection of peripheral malarial parasites in blood smears using deep learning models." Computational Intelligence and Neuroscience 2022 (2022)

    2022

  6. [15]

    Detection and classification of peripheral plasmodium parasites in blood smears using filters and machine learning algorithms

    Shetty, Vijaya, and VIJAYLAXMI KOCHARI. "Detection and classification of peripheral plasmodium parasites in blood smears using filters and machine learning algorithms." Proceedings http://ceur-ws. org ISSN 1613 (2023): 0073. 12

    2023

  7. [16]

    Comprehensive Evaluation and Insights into the Use of Large Language Models in the Automation of Behavior -Driven Development Acceptance Test Formulation

    Karpurapu, Shanthi, Sravanthy Myneni, Unnati Nettur, Likhit Sagar Gajja, Dave Burke, Tom Stiehm, and Jeffery Payne. "Comprehensive Evaluation and Insights into the Use of Large Language Models in the Automation of Behavior -Driven Development Acceptance Test Formulation." IEEE...

    2024

  8. [17]

    Cypress Copilot: Development of an AI Assistan t for Boosting Productivity and Transforming Web Application Testing

    Nettur, Suresh Babu, Shanthi Karpurapu, Unnati Nettur, and Likhit Sagar Gajja. "Cypress Copilot: Development of an AI Assistan t for Boosting Productivity and Transforming Web Application Testing." IEEE Access (2024)

    2024

  9. [18]

    A Hybrid Deep Learning CNN Model for Enhance d COVID -19 Detection from Computed Tomography (CT) Scan Images

    Nettur, Suresh B., Shanthi Karpurapu, Unnati Nettur, Likhit S. Gajja, Sravanthy Myneni, Akhil Dusi, and Lalithya Posham. "A Hybrid Deep Learning CNN Model for Enhance d COVID -19 Detection from Computed Tomography (CT) Scan Images." ArXiv, (2025). Accessed January 31, 2025. ht...

    2025 arXiv

  10. [19]

    Lightweight Weighted Average Ensemble Model for Pneumonia Detection in Chest X -Ray Images

    Nettur, Suresh B., Shanthi Karpurapu, Unnati Nettur, Likhit S. Gajja, Sravanthy Myneni, Akhil Dusi, and Lalithya Pos ham. "Lightweight Weighted Average Ensemble Model for Pneumonia Detection in Chest X -Ray Images." ArXiv, (2025). Accessed January 31, 2025. https://arxiv.org/a...

    2025 arXiv

  11. [20]

    Squeezenet: Alexnet -level accuracy with 50x fewer parameters and¡ 0.5 mb model size

    Forrest N Iandola, Song Han, Matthew W Moskewicz, Khalid Ashraf, William J Dally, and Kurt Keutzer. Squeezenet: Alexnet -level accuracy with 50x fewer parameters and¡ 0.5 mb model size. arXiv preprint arXiv:1602.07360, 2016

    2016 arXiv

  12. [21]

    Evaluations of deep convolutional neural networks for automatic identification of malaria infected cells

    Dong, Yuhang, Zhuocheng Jiang, Hongda Shen, W. David Pan, Lance A. Williams, Vishnu VB Reddy, William H. Benjamin, and Allen W. Bryan. "Evaluations of deep convolutional neural networks for automatic identification of malaria infected cells." In 2017 IEEE EMBS international co...

    2017

  13. [22]

    Malaria parasite detection from peripheral blood smear images using deep belief networks

    Bibin , Dhanya, Madhu S. Nair, and P. Punitha. "Malaria parasite detection from peripheral blood smear images using deep belief networks." IEEE Access 5 (2017): 9099-9108

    2017

  14. [23]

    Pre-trained convolutional neural networks as feature extractors toward improved malaria parasite detection in thin blood smear images

    Rajaraman, Sivaramakrishnan, Sameer K. Antani, Mahdieh Poostchi, Kamolrat Silamut, Md A. Hossain, Richard J. Maude, Stefan Jaeger, and George R. Thoma. "Pre-trained convolutional neural networks as feature extractors toward improved malaria parasite detection in thin blood sme...

    2018

  15. [24]

    Deep learning approach to detect malaria from microscopic images

    Vijayalakshmi, A. "Deep learning approach to detect malaria from microscopic images." Multimedia Tools and Applications 79, no. 21 (2020): 15297-15317

    2020

  16. [25]

    Classification of malaria cell images with deep learning architectures

    Çinar, Ahmet, and Muhammed Yildirim. "Classification of malaria cell images with deep learning architectures." Ingénierie des Systèmes d'Information 25, no. 1 (2020): 35

    2020

  17. [26]

    DeepFMD: computational analysis for malaria detection in blood - smear images using deep-learning features

    Abubakar, Aliyu, Mohammed Ajuji, and Ibrahim Usman Yahya. "DeepFMD: computational analysis for malaria detection in blood - smear images using deep-learning features." Applied System Innovation 4, no. 4 (2021): 82

    2021

  18. [27]

    A dataset and benchmark for malaria life -cycle classification in thin blood smear images

    Arshad , Qazi Ammar, Mohsen Ali, Saeed -ul Hassan, Chen Chen, Ayisha Imran, Ghulam Rasul, and Waqas Sultani. "A dataset and benchmark for malaria life -cycle classification in thin blood smear images." Neural Computing and Applications 34, no. 6 (2022): 4473- 4485

    2022

  19. [28]

    DSCN-net: a deep Siamese capsule neural network model for automatic diagnosis of malaria parasites detection

    Madhu, Golla, A. Govardhan, Vinayakumar Ravi, Sandeep Kautish, B. Sunil Srinivas, Tanupriya Chaudhary, and Manoj Kumar. "DSCN-net: a deep Siamese capsule neural network model for automatic diagnosis of malaria parasites detection." Multimedia Tools and Applications 81, no. 23 ...

    2022

  20. [29]

    A new ensemble learning approach to detect malaria from microscopic red blood c ell images

    Bhuiyan, Mosabbir, and Md Saiful Islam. "A new ensemble learning approach to detect malaria from microscopic red blood c ell images." Sensors International 4 (2023): 100209

    2023

  21. [30]

    Deep learning for smartphone -based malaria parasite detection in thi ck blood smears

    Yang, Feng, Mahdieh Poostchi, Hang Yu, Zhou Zhou, Kamolrat Silamut, Jian Yu, Richard J. Maude, Stefan Jaeger, and Sameer Antani. "Deep learning for smartphone -based malaria parasite detection in thi ck blood smears." IEEE journal of biomedical and health informatics 24, no. 5...

    2019

  22. [31]

    Visualizing deep learning activations for improved malaria cell classification

    Sivaramakrishnan, Rajaraman, Sameer Antani, and Stefan Jaeger. "Visualizing deep learning activations for improved malaria cell classification." In Medical informatics and healthcare, pp. 40 -47. PMLR, 2017

    2017

  23. [32]

    Malaria Diagnosis Using a Lightweight Deep Convolutional Neural Network

    Magotra, Varun, and Mukesh K. Rohil. "Malaria Diagnosis Using a Lightweight Deep Convolutional Neural Network." International Journal of Telemedicine and Applications 2022, no. 1 (2022): 4176982. Ac cessed January 7, 2025. https://doi.org/10.1155/2022/4176982

    2022 doi

  24. [33]

    Chaudhry, H.A.H., Farid, M.S., Fiandrotti, A. et al. A lightweight deep learning architecture for malaria parasite -type classification a nd life cycle stage detection. Neural Comput & Applic 36, 19795 –19805 (2024). https://doi.org/10.1007/s00521-024-10219-w

    2024 doi

  25. [34]

    Detection of Malaria Parasite Using Lightweight CNN Architecture and Smart Android Application

    Al Muazzaz, Hasan, and Omaer Faruq Goni. "Detection of Malaria Parasite Using Lightweight CNN Architecture and Smart Android Application." In 2024 IEEE International Conference on Power, Electrical, Electronics and Industrial Applications (PEEIACON), pp. 1-6. IEEE, 2024

    2024

  26. [35]

    A deep learning based framework for malaria diagnosis on high variation data set

    Zedda, Luca, Andrea Loddo, and Cecilia Di Ruberto. "A deep learning based framework for malaria diagnosis on high variation data set." In International Conference on Image Analysis and Processing, pp. 358-370. Cham: Springer International Publishing, 2022

    2022

  27. [36]

    Explainable AI Based Malaria Detection Using Lightweight CNN

    Goni, Md Omaer Faruq, and Md Nazrul Islam Mon dal. "Explainable AI Based Malaria Detection Using Lightweight CNN." In 2023 International Conference on Next -Generation Computing, IoT and Machine Learning (NCIM), pp. 1-5. IEEE, 2023

    2023

  28. [37]

    Generalized fractional optimization-based explainable lightweight CNN model for malaria disease classification

    Khan, Zeshan Aslam, Muhammad Waqar, Muhammad Junaid Ali Asif Raja, Naveed Ishtiaq Chaudhary, Abeer Tahir Mehmood Anwar Khan, and Muhammad Asif Zahoor Raja. "Generalized fractional optimization-based explainable lightweight CNN model for malaria disease classification." Compute...

    2025

  29. [38]

    https://www.kaggle.com/datasets/iarunava/cell -images-for-detecting- malaria

  30. [39]

    Embedded System‐Based Malaria Detection From Blood Smear Images Using Lightweight Deep Learning Model

    Salam, Abdus, SM Nahid Hasan, Md Jawadul Karim, Shamim Anower, Md Nahiduzzaman, Muhammad EH Chowdhury, and M. Murugappan. "Embedded System‐Based Malaria Detection From Blood Smear Images Using Lightweight Deep Learning Model." International Journal of Imaging Systems and Techn...

    2024

  31. [40]

    Mobile-Based Deep Convolutional Networks for Malaria Parasites Detection from Blood Cell Im ages

    Shahadat, Nazmul. "Mobile-Based Deep Convolutional Networks for Malaria Parasites Detection from Blood Cell Im ages." In 2023 26th International Conference on Computer and Information Technology (ICCIT), pp. 1-6. IEEE, 2023

    2023

  32. [41]

    Deep Malaria Parasite Detection in Thin Blood Smear Microscopic Images

    Maqsood, Asma, Muhammad Shahid Farid, Muhammad Hassan Khan, and Marcin Grzegorzek. 2021. "Deep Malaria Parasite Detection in Thin Blood Smear Microscopic Images" Applied Sciences 11, no. 5:

    2021

  33. [42]

    Deep Machine Learning Model Trade-Offs for Malaria Elimination i n Resource-Constrained locations

    Eze, Peter U., and Clement O. Asogwa. "Deep Machine Learning Model Trade-Offs for Malaria Elimination i n Resource-Constrained locations." Bioengineering 8, no. 11 (2021): 150. Accessed January 7, 2025. https://doi.org/10.3390/bioengineering8110150

    2021 doi

  34. [43]

    A Novel Shallow C onvNet-18 for Malaria Parasite Detection in Thin Blood Smear Images

    Elangovan, P., Nath, M.K. A Novel Shallow C onvNet-18 for Malaria Parasite Detection in Thin Blood Smear Images. SN COMPUT. SCI. 2, 380 (2021). https://doi.org/10.1007/s42979-021-00763-w

    2021 doi

  35. [44]

    Identification of mul tiple leaf diseases using improved SqueezeNet model

    Liu, Yang, and Guoqin Gao. "Identification of mul tiple leaf diseases using improved SqueezeNet model." Transactions of the chinese Society of Agricultural Engineering 37, no. 2 (2021): 187-195

    2021

  36. [45]

    "COVIDiagnosis-Net: Deep Bayes- SqueezeNet based diagnosis of the coronavirus disease 2019 (COVID-

    Ucar, Ferhat, and Deniz Korkmaz. "COVIDiagnosis-Net: Deep Bayes- SqueezeNet based diagnosis of the coronavirus disease 2019 (COVID-

    2019

  37. [46]

    An improved SqueezeNet model for the diagnosis of lung cancer in CT scans

    Tsivgoulis, Michail, Thomas Papastergiou, and Vasilis Megalooikonomou. "An improved SqueezeNet model for the diagnosis of lung cancer in CT scans." Machine Learning with Applications 10 (2022): 100399

    2022

  38. [47]

    from X-ray images." Medical hypotheses 140 (2020): 109761

    2020

  39. [48]

    Identification of tomato plant diseases by Leaf image using squeezenet model

    Hidayatuloh, Akbar, M. Nursalman, and Eki Nugraha. "Identification of tomato plant diseases by Leaf image using squeezenet model." In 2018 International Conference on Information Technology Systems and Innovation (ICITSI), pp. 199-204. IEEE, 2018

    2018

  40. [49]

    Real -time vehicle make and model recognition with the residual SqueezeNet architecture

    Lee, Hyo Jong, Ihsan Ullah, Weiguo Wan, Yongbin Gao, and Zhijun Fang. "Real -time vehicle make and model recognition with the residual SqueezeNet architecture." Sensors 19, no. 5 (2019): 982

    2019

  41. [50]

    Imagenet classification with deep convolutional neural networks

    Krizhevsky, Alex, Ilya Sutskever, and Geoffrey E. Hint on. "Imagenet classification with deep convolutional neural networks." Advances in neural information processing systems 25 (2012). 13 [51]https://pytorch.org/vision/main/models/generated/torchvision.models.s queezenet1_1.html

    2012

  42. [51]

    An electronic component recognition algorithm based on deep learning with a faster SqueezeNet

    Xu, Yuanyuan, Genke Yang, Jiliang Luo, and Jianan He. "An electronic component recognition algorithm based on deep learning with a faster SqueezeNet." Mathematical Problems in Engineering 2020, no. 1 (2020): 2940286

    2020

  43. [52]

    https://github.com/DeepScale/SqueezeNet

  44. [53]

    https://github.com/cmasch/squeezenet

  45. [54]

    With over two decades of expertise, he has established himself as a thought leader in artificial intelligence, deep learning, and machine learning solutions

    The github repo will be made available post acceptance of the paper SURESH BABU NETTUR has received his Master of Science (M.S) degree from the Birla Institute of Technology and Science (BITS), Pilani, Rajasthan, India, and his Bachelor of Technology degree in Computer Science...

    2017

  46. [2284]

    https://doi.org/10.3390/app11052284

    doi

Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.