Interpretable Deep Transfer Learning for Breast Ultrasound Cancer Detection: A Multi-Dataset Study

Mohammad Abbadi; Shadi Atalla; Wathiq Mansoor; Yassine Himeur

REVIEW 5 major objections 5 minor 30 references

Interpretable Deep Transfer Learning for Breast Ultrasound Cancer Detection: A Multi-Dataset Study

T0 review · 5 major / 5 minor · reviewed 2026-08-05 · deepseek-v4-flash

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Pith's one-line read ResNet-18 classifies breast ultrasound lesions at 99.7% accuracy with no missed malignancies in this multi-dataset study.

desk verdict A routine transfer-learning benchmark whose headline numbers are undercut by reporting gaps: no patient-level split stated, an impossible 95.2% on 100 images, and an unnamed external set. Reject as is, but not a waste of referee time. read the letter →

arxiv 2509.05004 v1 pith:KFA4XI6D submitted 2025-09-05 cs.CV

Mohammad Abbadi , Yassine Himeur , Shadi Atalla , Wathiq Mansoor This is my paper · ORCID

classification cs.CV

keywords breastultrasoundtransferlearningResNet-18deepfeaturesGrad-CAMcomputer-aideddiagnosisBUSIBUS-BRA

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

The paper tries to establish that transfer learning with off-the-shelf CNN architectures, especially ResNet-18, can classify breast ultrasound images into normal, benign, and malignant categories at near-perfect accuracy. Using the BUSI and BUS-BRA datasets, it reports 99.7% accuracy and 100% recall for malignant cases, and shows that deep features extracted from pretrained CNNs lift classical classifiers (SVM, KNN) close to CNN performance. The authors argue that this combination of high sensitivity, interpretability via Grad-CAM, and external validation supports practical AI-assisted breast cancer screening, particularly in resource-limited settings. The study is an application benchmark rather than a new method, but its headline result would matter clinically if it holds under rigorous patient-level evaluation.

What carries the argument

The central mechanism is transfer learning: pretrained ImageNet CNNs (ResNet-18, EfficientNet-B0, GoogLeNet, AlexNet, VGG16) are fine-tuned on breast ultrasound images after a preprocessing pipeline of grayscale normalization, median-filter speckle reduction, ROI cropping using masks, and data augmentation. Deep features extracted from the penultimate layers are also fed into SVM and KNN classifiers, showing that classical models gain substantially from learned representations. Grad-CAM is used to generate class-activation overlays that localize the image regions driving each prediction.

What would settle it

Run the same ResNet-18 preprocessing and training pipeline on BUSI using a strict patient-level split (ensuring no patient appears in both training and test) and report the test accuracy and malignant recall; if accuracy drops substantially below 99.7%, the headline result depends on image-level leakage rather than true generalization.

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Extended reading notes

Core claim

On the paper's own terms, the central discovery is that a standard ResNet-18 fine-tuned on breast ultrasound images achieves 99.7% classification accuracy and perfect sensitivity for malignant lesions on a test split of BUSI and BUS-BRA data. Deep-feature SVM reaches 99.3% accuracy, and EfficientNet-B0 achieves 99.5% with lower latency. External validation on an archive of 100 unseen images yields 95.2% accuracy, which the authors interpret as strong generalization with expected domain shift. The paper also demonstrates that Grad-CAM heatmaps align with diagnostically relevant regions, supporting the case that such models are interpretable enough for clinical second-reader roles.

Load-bearing premise

The 80/10/10 train-validation-test split is not explicitly stated to be at the patient level even though BUSI contains multiple images per patient, so images from the same patient may appear in both training and test sets, which would inflate the reported accuracy and perfect recall.

Editorial extensions

If this is right

If the 99.7% accuracy and perfect malignant recall hold under strict patient-level splits, a transfer-learned ResNet-18 could serve as a reliable second-reader tool in breast ultrasound screening workflows.
Deep features extracted from pretrained CNNs can make classical classifiers competitive with dedicated deep models, enabling deployment on mid-tier hardware in low-resource clinics.
Grad-CAM visualizations provide a practical interpretability layer that may increase radiologist trust and help flag ambiguous cases for manual review.
External validation at 95.2% accuracy quantifies a real performance drop under domain shift, indicating that multi-center training or adaptation is needed before clinical deployment.
The paper's model-selection rule, prioritizing malignant recall over overall accuracy, aligns the evaluation metric with clinical risk: missing a cancer is costlier than a false alarm.

Reading between the lines

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

The reported 99.7% accuracy likely depends on the exact train/test split construction; since BUSI has multiple images per patient, a patient-level split could lower the numbers substantially, and that remains untested here.
The paper's preprocessing includes ROI cropping from segmentation masks, which are not available in real-time clinical scans; an end-to-end detection framework would be needed to translate these results into practice.
Perfect malignant recall on a small test set is not strong evidence of zero false negatives at scale; testing on larger, more diverse cohorts with class imbalance would give a better estimate of the real-world miss rate.
If the external validation set was sampled from a single archive, the 95.2% accuracy may not generalize across scanner vendors or patient populations; multi-center prospective data would be the next test.

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Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

read the letter

Colleague,

Short version: this is not a new method—it's a standard transfer-learning bake-off on two public ultrasound datasets. The reported ResNet-18 numbers (99.7% accuracy, perfect malignant recall) are plausible given prior work, but the paper as written does not support them. I'd send it back for major revisions, or reject if the authors can't fix the reporting.

What's genuinely useful: the paper gives a clear, step-by-step pipeline (preprocessing, feature extraction, model training, evaluation, Grad-CAM) and a sensible comparison of classical ML with and without deep features, CNNs of varying depth, and inference-time considerations. The emphasis on malignant recall as the selection criterion is clinically sane. If the numbers were reproducible, this would be a convenient benchmark for practitioners scoping CAD systems.

The soft spots are not minor. First, no patient-level split is stated despite BUSI having 780 images from 600 patients. Algorithm 1 says 'stratified 80/10/10', which sounds class-stratified, not patient-stratified. If images from the same patient leak across train and test, the headline accuracy is inflated. Second, the external validation claim of 95.2% on '100 images' is arithmetically impossible with whole-image counts. That kind of sloppiness makes me distrust every number in Table I. Third, the external archive is never named, so reproducibility is nil. Fourth, there are no confidence intervals or error bars. Fifth, several references (e.g., [12], [14], [15], [17], [18]) look like placeholder or unverifiable entries; that's a citation-hygiene problem that undermines the lit review.

None of this is fatal in the sense of a flawed idea—the methodology is standard and the framing is coherent. But the evidence for the 'state-of-the-art' claim is weak as presented. The paper adds no new technique, loss, or theoretical insight; its contribution is a benchmark number, and that number isn't verifiable.

Who would get value? Applied researchers or clinicians wanting a quick survey and a template pipeline. But they should not rely on the absolute numbers without code and data. I'd bring it to a reading group as a cautionary example, and I'd be willing to referee a substantially revised version if the authors clarify the split, name the external set, fix the arithmetic, and release code. As it stands, I would not cite it.

Recommendation: desk reject in current form, or conditional reject with explicit invitation to resubmit after addressing leakage and reporting. Either way, don't take the 99.7% at face value.

Referee Report

5 major / 5 minor

Summary. The paper evaluates classical ML (SVM, KNN) and deep transfer-learning CNNs (AlexNet, VGG16, GoogLeNet, ResNet-18, EfficientNet-B0) for three-class breast ultrasound classification (normal/benign/malignant). The central empirical claim is that ResNet-18 reaches 99.7% accuracy with perfect recall for malignant lesions on a combined BUSI/BUS-BRA evaluation, with an external validation accuracy of 95.2%. The paper also compares deep-feature SVM/KNN, provides Grad-CAM interpretability, and proposes an algorithmic pipeline for clinical deployment.

Significance. If the reported results are reproducible and leakage-free, the paper would provide a strong benchmark: a standard transfer-learned ResNet-18 with high sensitivity on public breast ultrasound datasets, plus interpretability via Grad-CAM and a usable algorithmic formulation. The comparison between classical classifiers with deep features and full CNNs is useful, and the use of publicly available BUSI and BUS-BRA datasets is a strength. However, the significance is conditional on resolving serious reporting gaps: no patient-level split is documented, no confidence intervals are given, the external validation set is unnamed and arithmetically inconsistent, and the multi-dataset framing exceeds the actual experiments. The paper does not release code or patient identifiers, so the central claim is currently unverifiable.

major comments (5)

[II-A, II-E, Algorithm 1] BUSI contains 780 images from 600 patients (Section II-A). The split description (Section II-E and Algorithm 1, Step 1) states only a 'stratified 80/10/10' split, with class labels as the stratification variable, and does not state that all images from a given patient are kept in the same partition. If one patient contributes multiple images, near-duplicate views of the same lesion can appear in both training and test, inflating the 99.7% accuracy and perfect malignant recall in Table I. Please specify a patient-level split, report the number of patients per partition, and provide patient-clustered metrics. If such a split was used, state it explicitly; if not, rerun the experiments.
[III-A, Table I] Table I reports point estimates only, with no confidence intervals, fold-level standard deviations, or statistical comparisons among models. Section II-E mentions both 5-fold cross-validation and 80/10/10 train-validation-test splits, but it is not stated whether Table I reports a single held-out test set, an average over folds, or a specific split. For a headline claim of 99.7% accuracy and recall 1.00, binomial or bootstrap confidence intervals are necessary to assess whether the differences among ResNet-18, GoogLeNet, and EfficientNet-B0 are meaningful.
[III-C] The external validation reports '100 images' and 95.2% accuracy. On a single set of 100 examples, accuracy must be a multiple of 1% (95% or 96%), so 95.2% is arithmetically inconsistent unless the value is an average over folds/subsets or the total is not 100. The archive itself is also unnamed. Please identify the external dataset, give the exact number of images and patients, provide the confusion matrix, and reconcile the reported accuracy with the sample count. This matters because the external validation is the primary evidence for the generalization claim.
[II-A and Abstract/Contributions] The abstract and contribution list announce a 'multi-dataset' study, but a later paragraph in Section II-A states that the reported results were 'derived from experiments on the BUSI and BUS-BRA datasets' and that BrEaST-Lesions USG, the Breast Ultrasound Archive, and the Hybrid dataset were used only for segmentation, augmentation, or external validation. Table I indeed covers only BUSI and BUS-BRA. Please either present per-dataset results for every dataset used or revise the framing so that 'multi-dataset' accurately describes the actual experiments.
[Algorithm 1, Step 8] Algorithm 1 selects the best model by maximizing malignant recall, AUC, and accuracy. The text does not clarify whether this selection is performed on the validation split or on the test split. If the test set is used for model selection, the reported superiority of ResNet-18 is selection bias, not an independent comparison. Please state the selection protocol explicitly and, if test data influenced the choice of architecture, either correct the claim or report validation-based selection results.

minor comments (5)

[II-B] The sentence 'zoom up to 10' is incomplete; specify whether the zoom range is 10% or 10 pixels and how it is implemented.
[II-E] The text refers to 'Algo.??' as an unresolved placeholder; Algorithm 1 is referenced before it is introduced. Please fix the cross-reference.
[II-A] The paragraph beginning 'In this study, we indeed discussed five datasets...' reads as an inserted clarification rather than part of the Methods. Please integrate this information into the experimental design description and keep it consistent with the abstract.
[Figures] Figures 1 and 2 are referenced but not included in the text I received. Ensure the final version contains the confusion matrix and ROC curves with labeled axes and legends.
[IV-C] The Limitations section should explicitly address the absence of patient-level split documentation and the lack of confidence intervals, as these directly affect the interpretation of the reported near-perfect results.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the headline result is an empirical benchmark, not a derivation from fitted inputs or self-citations.

full rationale

The paper's central claim—ResNet-18 achieves 99.7% accuracy and perfect malignant recall on BUSI/BUS-BRA (Section III-A, Table I)—is an empirical evaluation result, not a derivation. The pipeline (Algorithm 1) trains models on 80/10/10 or official splits and reports test metrics; no parameter is constructed so that the reported quantity equals it by definition. The self-citations (e.g., refs [1], [3]–[5], [9]–[11], [13], [22], [23]) appear in background or related-work contexts and are not load-bearing for the experimental outcome. The paper does not invoke a uniqueness theorem or ansatz from prior work to force model choice. The strongest caveats are methodological rather than circular: the paper never states that the BUSI split is patient-level even though BUSI has 780 images from 600 patients, so possible patient leakage could bias the headline number; and 95.2% accuracy on 100 external images is numerically inconsistent with integer counts (95/100 or 96/100). These are correctness/reporting concerns, not cases where a 'prediction' reduces by construction to its inputs.

Assumptions & free parameters 0 free parameters · 4 assumptions · 0 invented entities

The paper relies on standard transfer learning and publicly available datasets. No new free parameters are fit to force the result, and no new entities are invented. The key hidden assumption is the patient-level data split, which is unstated and critical.

assumptions (4)

domain assumption Images in BUSI and BUS-BRA are representative of clinical breast ultrasound and labels are correct.
The central accuracy claim depends on label quality and dataset representativeness.
domain assumption The 80/10/10 split is performed at the patient level, despite the paper not stating this.
BUSI has 780 images from 600 patients; if split at image level, same-patient images could leak across sets and inflate accuracy. The paper does not describe any patient-level grouping.
domain assumption Preprocessing (median filter, resizing, normalization) does not remove diagnostically relevant information.
The models are trained on preprocessed images; if preprocessing discards lesion texture, the reported accuracy would differ. This is a domain assumption about the ultrasound modality.
domain assumption Grad-CAM heatmaps correctly localize diagnostically relevant regions.
Interpretability claims rest on the assumption that gradients reflect clinically meaningful regions, but no quantitative evaluation of the heatmaps is provided.

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Cite this review

Pith. "Pith review of Interpretable Deep Transfer Learning for Breast Ultrasound Cancer Detection: A Multi-Dataset Study." pith.science (2026). https://pith.science/paper/KFA4XI6D

@misc{pith2026250905004,
  author       = {Pith},
  title        = {Pith review of: Interpretable Deep Transfer Learning for Breast Ultrasound Cancer Detection: A Multi-Dataset Study},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/KFA4XI6D}},
  note         = {Machine review of arXiv:2509.05004}
}

read the original abstract

Breast cancer remains a leading cause of cancer-related mortality among women worldwide. Ultrasound imaging, widely used due to its safety and cost-effectiveness, plays a key role in early detection, especially in patients with dense breast tissue. This paper presents a comprehensive study on the application of machine learning and deep learning techniques for breast cancer classification using ultrasound images. Using datasets such as BUSI, BUS-BRA, and BrEaST-Lesions USG, we evaluate classical machine learning models (SVM, KNN) and deep convolutional neural networks (ResNet-18, EfficientNet-B0, GoogLeNet). Experimental results show that ResNet-18 achieves the highest accuracy (99.7%) and perfect sensitivity for malignant lesions. Classical ML models, though outperformed by CNNs, achieve competitive performance when enhanced with deep feature extraction. Grad-CAM visualizations further improve model transparency by highlighting diagnostically relevant image regions. These findings support the integration of AI-based diagnostic tools into clinical workflows and demonstrate the feasibility of deploying high-performing, interpretable systems for ultrasound-based breast cancer detection.

Figures

Figures reproduced from arXiv: 2509.05004 by the authors.

**Figure 2.** ROC curves for selected models. These results demonstrate that with appropriate architecture, preprocessing, and dataset diversity, AI systems can reach or exceed expert-level accuracy in ultrasound-based breast cancer detection [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

30 extracted references · 28 canonical work pages

[12]

Deep learning for breast cancer detection in ultrasound im- ages: A comprehensive review,

L. Zhang, “Deep learning for breast cancer detection in ultrasound im- ages: A comprehensive review,”Journal of Imaging Research, vol. 23, no. 4, pp. 300–310, 2021

work page 2021
[14]

Hybrid models for breast cancer detection: Combining CNNs and traditional ML approaches,

M. Liu, “Hybrid models for breast cancer detection: Combining CNNs and traditional ML approaches,”Journal of Medical AI, vol. 19, no. 3, pp. 110–120, 2022

work page 2022
[15]

AI-based feature extraction using transfer learning for breast cancer detection,

N. Patel, “AI-based feature extraction using transfer learning for breast cancer detection,”International Journal of Medical AI, vol. 14, pp. 123–132, 2021

work page 2021
[17]

Recent advances in ML algorithms for breast cancer detec- tion,

D. Wang, “Recent advances in ML algorithms for breast cancer detec- tion,”Journal of Cancer Research, vol. 25, pp. 300–315, 2023

work page 2023
[18]

Comparative analysis of deep learning and traditional ML methods for breast cancer detection,

H. Sun, “Comparative analysis of deep learning and traditional ML methods for breast cancer detection,”Journal of Healthcare AI, vol. 20, no. 2, pp. 50–60, 2022

work page 2022
[1]

Ai in thyroid cancer diagnosis: Techniques, trends, and future directions,

Y . Habchi, Y . Himeur, H. Kheddar, A. Boukabou, S. Atalla, A. Chouchane, A. Ouamane, and W. Mansoor, “Ai in thyroid cancer diagnosis: Techniques, trends, and future directions,”Systems, vol. 11, no. 10, p. 519, 2023

work page 2023
[2]

Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries,

H. Sung, J. Ferlay, R. L. Siegel, M. Laversanne, I. Soerjomataram, A. Jemal, and F. Bray, “Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries,”CA: A Cancer Journal for Clinicians, vol. 71, no. 3, pp. 209–249, 2021

work page 2020
[3]

Gflasso-lr: logistic regres- sion with generalized fused lasso for gene selection in high-dimensional cancer classification,

A. Bir-Jmel, S. M. Douiri, S. E. Bernoussi, A. Maafiri, Y . Himeur, S. Atalla, W. Mansoor, and H. Al-Ahmad, “Gflasso-lr: logistic regres- sion with generalized fused lasso for gene selection in high-dimensional cancer classification,”Computers, vol. 13, no. 4, p. 93, 2024

work page 2024

Show all 30 references

[4]

Hybrid whale-mud-ring optimization for precise color skin cancer image segmentation,

A. Hamza, B. Lekouaghet, and Y . Himeur, “Hybrid whale-mud-ring optimization for precise color skin cancer image segmentation,” in2023 6th International Conference on Signal Processing and Information Security (ICSPIS). IEEE, 2023, pp. 87–92

2023
[5]

Advanced deep learning and large language models: Comprehensive insights for cancer detection,

Y . Habchi, H. Kheddar, Y . Himeur, A. Belouchrani, E. Serpedin, F. Khelifi, and M. E. Chowdhury, “Advanced deep learning and large language models: Comprehensive insights for cancer detection,”Image and Vision Computing, vol. 157, p. 105495, 2025

2025
[6]

Practical ultrasound evaluation of breast lesions,

E. B. Mendelson, “Practical ultrasound evaluation of breast lesions,” Radiologic Clinics of North America, vol. 52, no. 6, pp. 1205–1220, 2014

2014
[7]

Artificial intelligence in breast imaging,

E. P. Le, Y . Wang, Y . Huang, S. Hickman, and F. J. Gilbert, “Artificial intelligence in breast imaging,”Clinical Radiology, vol. 74, no. 5, pp. 357–366, 2019

2019
[8]

Impact of variability in ultrasound imaging on breast cancer diagnosis: A survey,

M. Ghaffari, A. Sowmya, and A. Oliver, “Impact of variability in ultrasound imaging on breast cancer diagnosis: A survey,”Ultrasound in Medicine & Biology, vol. 46, no. 4, pp. 876–888, 2020

2020
[9]

Harness- ing transformers: A leap forward in lung cancer image detection,

A. Bechar, Y . Elmir, R. Medjoudj, Y . Himeur, and A. Amira, “Harness- ing transformers: A leap forward in lung cancer image detection,” in 2023 6th International Conference on Signal Processing and Informa- tion Security (ICSPIS). IEEE, 2023, pp. 218–223

2023
[10]

Federated and transfer learning for cancer detection based on image analysis,

A. Bechar, R. Medjoudj, Y . Elmir, Y . Himeur, and A. Amira, “Federated and transfer learning for cancer detection based on image analysis,” Neural Computing and Applications, vol. 37, no. 4, pp. 2239–2284, 2025

2025
[11]

Enhanc- ing cancer detection with fine-tuned large language models: A compar- ative study on low-rank adaptation,

A. Bechar, Y . Elmir, Y . Himeur, R. Medjoudj, and A. Amira, “Enhanc- ing cancer detection with fine-tuned large language models: A compar- ative study on low-rank adaptation,” in2024 IEEE/ACM International Conference on Big Data Computing, Applications and Technologies (BDCAT...

2024
[13]

Ultrasound images classi- fication of thyroid cancer using deep transfer learning,

Y . Habchi, H. Kheddar, and Y . Himeur, “Ultrasound images classi- fication of thyroid cancer using deep transfer learning,” in2024 In- ternational Conference on Telecommunications and Intelligent Systems (ICTIS). IEEE, 2024, pp. 1–6

2024
[16]

Attention-based convolutional neural networks for breast cancer detection in ultrasound images,

S. Kimet al., “Attention-based convolutional neural networks for breast cancer detection in ultrasound images,”Medical Image Analysis, vol. 86, p. 102803, 2023

2023
[19]

Multicenter evaluation of deep learning for breast ultrasound diagnosis,

Y . Xianget al., “Multicenter evaluation of deep learning for breast ultrasound diagnosis,”Radiology: Artificial Intelligence, vol. 5, no. 2, p. e220214, 2023

2023
[20]

Interpretable deep learning model for breast ultrasound diagnosis in clinical practice,

H. Yanet al., “Interpretable deep learning model for breast ultrasound diagnosis in clinical practice,”Nature Medicine, vol. 30, no. 2, pp. 210– 218, 2024

2024
[21]

Deep radiomics of ultrasound images for breast lesion classification,

J. Liuet al., “Deep radiomics of ultrasound images for breast lesion classification,”IEEE Transactions on Medical Imaging, vol. 43, no. 1, pp. 101–110, 2024

2024
[22]

Deep transfer learning for kidney cancer diagnosis,

Y . Habchi, H. Kheddar, Y . Himeur, M. C. Ghanem, A. Boukabou, S. Atalla, W. Mansoor, and H. Al-Ahmad, “Deep transfer learning for kidney cancer diagnosis,”arXiv preprint arXiv:2408.04318, 2024

2024 arXiv
[23]

Transfer learning for cancer detection based on images analysis,

A. Bechar, Y . Elmir, R. Medjoudj, Y . Himeur, and A. Amira, “Transfer learning for cancer detection based on images analysis,”Procedia Computer Science, vol. 239, pp. 1903–1910, 2024

1903
[24]

Dataset of breast ultrasound images,

W. Al-Dhabyani, M. Gomaa, H. Khaled, and A. Fahmy, “Dataset of breast ultrasound images,”Data in Brief, vol. 28, p. 104863, 2020

2020
[25]

BUS- BRA: A breast ultrasound dataset for assessing computer-aided diag- nosis systems,

W. Gómez-Flores, M. J. Gregorio-Calas, and W. C. A. Pereira, “BUS- BRA: A breast ultrasound dataset for assessing computer-aided diag- nosis systems,”Medical Physics, vol. 51, no. 4, pp. 3110–3123, 2024

2024
[26]

Efficientnet: Rethinking model scaling for convolutional neural networks,

M. Tan and Q. V . Le, “Efficientnet: Rethinking model scaling for convolutional neural networks,” inProceedings of the 36th International Conference on Machine Learning (ICML). PMLR, 2019, pp. 6105– 6114, pMLR V ol. 97

2019
[27]

Breast cancer classification from ultrasound images using probability-based optimal deep learning feature fusion,

K. Jabeen, M. A. Khan, M. Alhaisoni, T. Saba, and A. Rehman, “Breast cancer classification from ultrasound images using probability-based optimal deep learning feature fusion,”Sensors, vol. 22, no. 3, p. 807, 2022

2022
[28]

CAM-based explainable deep learning models for medical image classification: A review,

Q. Jin, Y . Wang, X. Yang, and F. He, “CAM-based explainable deep learning models for medical image classification: A review,”IEEE Reviews in Biomedical Engineering, vol. 16, pp. 62–75, 2023

2023
[29]

Object detection in breast ultrasound using YOLOv5 and clinically interpretable evaluation,

Y . Oh, J. Park, and Y . Park, “Object detection in breast ultrasound using YOLOv5 and clinically interpretable evaluation,”Computers in Biology and Medicine, vol. 136, p. 104649, 2021

2021
[30]

The future of digital health with federated learning,

N. Riekeet al., “The future of digital health with federated learning,” NPJ Digital Medicine, vol. 3, no. 1, pp. 1–7, 2020

2020

Pith tools

Reviewed August 5, 2026 · model on record in the stance chip above.

[1] [12]

Deep learning for breast cancer detection in ultrasound im- ages: A comprehensive review,

L. Zhang, “Deep learning for breast cancer detection in ultrasound im- ages: A comprehensive review,”Journal of Imaging Research, vol. 23, no. 4, pp. 300–310, 2021

work page 2021

[2] [14]

Hybrid models for breast cancer detection: Combining CNNs and traditional ML approaches,

M. Liu, “Hybrid models for breast cancer detection: Combining CNNs and traditional ML approaches,”Journal of Medical AI, vol. 19, no. 3, pp. 110–120, 2022

work page 2022

[3] [15]

AI-based feature extraction using transfer learning for breast cancer detection,

N. Patel, “AI-based feature extraction using transfer learning for breast cancer detection,”International Journal of Medical AI, vol. 14, pp. 123–132, 2021

work page 2021

[4] [17]

Recent advances in ML algorithms for breast cancer detec- tion,

D. Wang, “Recent advances in ML algorithms for breast cancer detec- tion,”Journal of Cancer Research, vol. 25, pp. 300–315, 2023

work page 2023

[5] [18]

Comparative analysis of deep learning and traditional ML methods for breast cancer detection,

H. Sun, “Comparative analysis of deep learning and traditional ML methods for breast cancer detection,”Journal of Healthcare AI, vol. 20, no. 2, pp. 50–60, 2022

work page 2022

[6] [1]

Ai in thyroid cancer diagnosis: Techniques, trends, and future directions,

Y . Habchi, Y . Himeur, H. Kheddar, A. Boukabou, S. Atalla, A. Chouchane, A. Ouamane, and W. Mansoor, “Ai in thyroid cancer diagnosis: Techniques, trends, and future directions,”Systems, vol. 11, no. 10, p. 519, 2023

work page 2023

[7] [2]

Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries,

H. Sung, J. Ferlay, R. L. Siegel, M. Laversanne, I. Soerjomataram, A. Jemal, and F. Bray, “Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries,”CA: A Cancer Journal for Clinicians, vol. 71, no. 3, pp. 209–249, 2021

work page 2020

[8] [3]

Gflasso-lr: logistic regres- sion with generalized fused lasso for gene selection in high-dimensional cancer classification,

A. Bir-Jmel, S. M. Douiri, S. E. Bernoussi, A. Maafiri, Y . Himeur, S. Atalla, W. Mansoor, and H. Al-Ahmad, “Gflasso-lr: logistic regres- sion with generalized fused lasso for gene selection in high-dimensional cancer classification,”Computers, vol. 13, no. 4, p. 93, 2024

work page 2024