arXiv AI

A Lightweight CNN Integrated Compact Convolutional Transformer for Multi-Scale Feature Learning and reducing computational complexity for breast cancer mammography image detection and classification

The paper presents a lightweight CNN‑integrated Compact Convolutional Transformer (CCT) designed for multi‑scale feature learning in breast cancer mammography. With only 250,435 parameters, the model achieved 99‑100% accuracy across three datasets using 5‑fold cross‑validation, demonstrating robust generalization. Explainable AI components were added to clarify the classification process, aiming to increase clinical trust in resource‑constrained settings.

arXiv Machine Learning

Jul 14

BiLoG-Net: A Bi-Context Location-Guided Network for Breast Mass Segmentation and Malignancy Classification in Mammography

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

llms computer-vision benchmarks

Hugging Face Trending Papers

Jun 21

Multi-cancer detection using a computationally efficient CNN with transfer learning

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.

fine-tuning benchmarks

arXiv AI

4d ago

ProtoCAM: Interpretable Few-Shot Mask-Guided Prototypical Learning for Breast Lesion Classification in Ultrasound Imaging

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

rag

arXiv AI

Aug 12

Energy and Performance Benchmarking of Deep Learning Models for Breast Cancer Detection

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

llms benchmarks

arXiv AI

Jul 16

Beyond Backbone Backpropagation: A Decoupled Strategy for Efficient Transfer Learning

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

llms computer-vision fine-tuning benchmarks

arXiv AI

Jun 9

AMN: An Adaptive Multi-Scale Fusion Network with Boundary and Uncertainty Modeling for Nuclei Segmentation

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

llms computer-vision benchmarks

arXiv AI

Aug 25

Robust Lightweight Deep Learning Models for Oral Cancer Screening

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

llms efficiency benchmarks safety data-engineering

arXiv AI

Aug 18

DualMiT-Net: Local-Global Transformer-Convolutional Fusion for Breast Mass Segmentation in Mammographic Regions of Interest

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

llms computer-vision

arXiv Machine Learning

Sep 11

Artificial Intelligence Algorithms for the Detection of Pathologies Related to Lung Cancer through Image Analysis using Convolutional Neural Networks and Data Augmentation: a systematic mapping of the literature

The article reviews recent applications of artificial intelligence and deep learning, particularly convolutional neural networks with transfer learning and data augmentation, for detecting lung cancer in medical images. It surveys 96 studies from 2015 onward, highlighting high sensitivity and specificity achieved by these methods. The authors also identify challenges such as data standardization, model explainability, patient privacy, and ethical concerns that must be addressed before clinical deployment.

By Pablo Ramirez Amador

fine-tuning safety

arXiv Computer Vision

Sep 11

PathoHR: Breast Cancer Survival Prediction on High-Resolution Pathological Images

PathoHR is a new pipeline for predicting breast cancer survival from high‑resolution pathological images. It uses a plug‑and‑play Vision Transformer to enhance patch‑wise whole slide image representations, evaluates multiple similarity metrics to optimize feature learning, and shows that smaller, enhanced patches can match or surpass the accuracy of larger raw patches while cutting computational cost. The authors provide experimental evidence that this approach improves both accuracy and efficiency in computational pathology.

By Yang Luo, Shiru Wang, Jun Liu, Jiaxuan Xiao, Rundong Xue, Zeyu Zhang, Hao Zhang, Yu Lu, Yang Zhao, Yutong Xie

llms computer-vision

arXiv Machine Learning

Jul 27

CARDIAG: A Dense Segment Classification Benchmark of Deep Learning Architectures for Coronary Angiography

arXiv:2607. 22139v1 Announce Type: cross Abstract: Accurate pixel-level classification of coronary angiograms is critical for cardiovascular disease assessment, yet the field lacks standardized evaluation protocols.

By Dominik Bernard Lau, Hubert Malinowski, Jerzy Szyjut, Adam Brzeski, Tomasz Dziubich, Rados{\l}aw Targo\'nski, Tomasz Figatowski, Natalia Zieli\'nska

computer-vision benchmarks

arXiv Machine Learning

Jul 30

Classification of Disease from Lungs X-ray Images using VGG16, VGG19 and ResNet50 Models

arXiv:2607. 26580v1 Announce Type: cross Abstract: With the increase in the number of cases related to respiratory diseases, there is an urgent need to detect them early and diagnose them accurately.

By Nand Lal Yadav, Rajesh Kumar, Satyendra Singh, Sudhakar Singh

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