The study evaluates ten ImageNet‑pretrained deep learning models for classifying benign versus malignant breast cancer in the BreakHis histopathology dataset, using a patient‑level split. DenseNet121 achieved the highest performance with 99.4% accuracy and an AUC‑ROC of 0.999, outperforming all other models (p < 0.001). Explainability methods (Grad‑CAM, LIME, Integrated Gradients, LRP) consistently identified nuclei‑rich and glandular regions as key diagnostic features, demonstrating that transfer learning combined with explainable AI can produce accurate and interpretable breast cancer diagnostic tools.
By Sami Alnawali, Muhammad Atif Saeed, Abdul Hadi, Azka Atiq, Akhtar Jamil, Ahmad Din
arXiv:2607. 10188v1 Announce Type: cross Abstract: Breast cancer remains the most commonly diagnosed malignancy among women worldwide, yet accurate detection and characterization of breast masses in mammography remain challenging due to subtle intensity variations, heterogeneous tissue densities, and indistinct lesion boundaries that complicate radiological interpretation.
By Abu Fatema Mohammad Abdun Noor, Md Imam Ahasan, Md Samiul Ahasan, Kah Ong Michael Goh, S M Hasan Mahmud, Raihana Zannat
arXiv:2503.13309v3 Announce Type: replace-cross
Abstract: Despite advancements in Computer-Aided Diagnosis (CAD) systems, breast cancer remains one of the leading causes of cancer-related deaths amon...
By Farnoush Bayatmakou, Reza Taleei, Milad Amir Toutounchian, Arash Mohammadi
This study introduces a computationally efficient convolutional neural network (CNN) architecture enhanced with transfer learning for multi-cancer detection using biomedical images. The proposed lightweight CNN model is designed to reduce computational complexity while maintaining high classification performance, making it suitable for deployment in resource-constrained environments.
ProtoCAM is an explainable few‑shot learning framework for classifying breast lesions in ultrasound images. It combines mask‑guided feature encoding, prototypical metric learning, and gradient‑based visual explanations to leverage limited annotated data. Evaluated on the BUSI dataset, ProtoCAM achieved a macro F1‑score of 0.910 in a 3‑way 5‑shot setting, outperforming standard supervised CNNs, with ResNet18 reaching 91.65% under 15‑shot conditions.
By Ashkan Ebadi
The paper introduces a Hybrid Cross-Modal Attention Network (HCMAN) that fuses mammogram images with structured clinical data using transformer-based cross‑modal attention. Trained on a locally collected dataset of 2,560 images from 1,024 Ethiopian patients, the model achieves 97.8% accuracy, 97.2% sensitivity, 98.3% specificity, and an AUC of 0.987, outperforming image‑only baselines and maintaining robustness to low‑quality images. Its lightweight design allows inference in under two seconds on a standard CPU, making it suitable for deployment in resource‑limited clinical settings.
By Simon Hadush Nrea (Mekelle University, Mekelle, Ethiopia), Filimon Gidey Gebremichael (Mekelle University, Mekelle, Ethiopia), Gebrekirstos Hagos Gebrekirstos (Clinical Oncologist London School of Hygiene and Tropical Medicine London, UK), Yaecob Girmay Gezahegn (Mekelle University, Mekelle, Ethiopia)
M3D‑Net is a mammography encoder that hierarchically coordinates multi‑scale coordinate attention, bounded dynamic feature reuse, and differential attention through resolution‑aware operator placement. It preserves earlier features within stages, integrates local and global context via coordinate‑aware aggregation, and applies differential attention at coarse resolutions. In image‑only classification on AISSLab mammography and an adapted image‑clinical model on BrEaST ultrasound, M3D‑Net achieves the highest validation accuracy and lowest endpoint cross‑entropy loss compared to EdgeNeXt, RepViT, and TransXNet, with accuracies of 97.78% and 80.39% respectively.
By Zheng Yu, Xinhang Li, Jiabao Gao, Boyang Wang, Xiang Li
arXiv:2608. 09996v1 Announce Type: cross Abstract: Recent advances in machine learning have greatly improved breast cancer detection, enabling more accurate and timely diagnosis.
By Samar Garrab, Ghada Achour
arXiv:2607. 13043v1 Announce Type: cross Abstract: Deep learning models achieve state-of-the-art image classification but face deployment challenges due to computational costs and energy demands.
By Daniel Vila-Cruz, Laura Mor\'an-Fern\'andez, Ver\'onica Bol\'on-Canedo
arXiv:2606. 07633v1 Announce Type: cross Abstract: Accurate classification of nuclei subtypes in histopathology images is critical for downstream tasks including tumor grading, immune infiltrate quantification, and prognosis prediction.
By Spoorthi M, Suja Palaniswamy
arXiv:2608.21583v1 Announce Type: new
Abstract: Oral cancer is a leading cause of mortality in low-to-middle-income countries, where a shortage of specialists delays diagnosis. While point-of-care sc...
By Siddhant Bharadwaj, Aakash Shedsale, Tejashree Subramanya, Mohd. Azfar, Praveen Birur, Debnath Pal, Shankararama Sharma, Anupama Shetty, Rajesh Sundaresan
arXiv:2608. 15019v1 Announce Type: cross Abstract: Breast mass segmentation is an important step in computer-aided mammography, but it remains difficult because masses can have low contrast, irregular shapes, and boundaries that blend with surrounding breast tissue.
By Alibek Kamiluly, Milana Muratova, Yash Patel, Fan Li