arXiv Machine Learning October 7, 2026 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)

Hybrid Cross-Modal Attention Network for Early Breast Cancer Detection in Low-Resource Clinical Settings

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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.

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arXiv Computer Vision

Sep 25

OncoVision: Integrating Mammography and Clinical Data through Attention-Driven Multimodal AI for Enhanced Breast Cancer Diagnosis

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computer-vision multimodal

arXiv Machine Learning

Jul 14

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llms computer-vision benchmarks

arXiv Machine Learning

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computer-vision

arXiv AI

Sep 17

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By Md Taimur Ahad (Department of Management North South University, Dhaka, Bangladesh), Ainuddin Ahmed (Department of Management North South University, Dhaka, Bangladesh)

llms rag nlp

arXiv AI

Aug 18

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llms computer-vision

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rag fine-tuning multimodal