[Medical prior-guided TabMap deep learning model for ovarian cancer prediction and interpretability analysis].

An Explainable Hybrid Machine Learning Framework for Enhanced Early Detection and Comprehensive Staging of Breast Cancer

An Explainable Hybrid Machine Learning (XML) framework that integrates advanced feature extraction with interpretable classification techniques for early breast cancer detection and staging and offers a robust, transparent, and clinically auditable solution for personalized breast cancer diagnosis and treatment plannin...

Improving Thyroid Cancer Diagnosis and Risk Stratification with VGGNet-Driven Deep Learning from Multimodal Imaging

Recent developments in deep learning have demonstrated potential in improving the analysis of medical images in diagnosing cancer. This paper tackles the problem of precise diagnosis of thyroid cancer and stratification of risk based on multimodal imaging data. We suggest a deep-learning structure using the VGGNet fram...

A Hybrid CNN-SVM Model with Attention Mechanisms for Chronic Kidney Disease Prediction

This study addresses the limitations of traditional diagnostic methods and the "black-box" nature of standard deep learning by developing an explainable, attention-driven hybrid Convolutional Neural Network- Support Vector Machine (CNN-SVM) framework that provides a robust, transparent, and low-cost diagnostic utility.

AI-driven diagnosis of lung and colorectal cancers with explainable deep learning on histopathological images

The combination of deep learning with XAI gives an interpretable, high-performance approach for early cancer diagnosis, which can benefit doctors in making better informed diagnostic judgements, improving patient outcomes.

Multi-class lung cancer detection and classification from CT scans using CNN transformer

Uncertainty estimation of breast cancer classification using deep learning methods with explainable artificial intelligence

Globally, breast cancer-related mortality among women requires early and precise diagnostic support to improve patient outcomes. The existing research work discusses using Machine learning and deep learning models for precise diagnosis with a combination of classified outcomes using various datasets. The proposed work...