Enhancing Cancer Diagnosis with Explainable & Trustworthy Deep Learning Models

TL;DR

This paper introduces an explainable deep learning model for cancer diagnosis that improves interpretability, accuracy, and accessibility, aiming to enhance early detection and personalized treatment while building trust among healthcare providers.

Contribution

It develops an innovative XAI-based neural network model that addresses the black box problem in cancer diagnosis, improving transparency and trust in AI-driven medical decisions.

Findings

Enhanced interpretability of cancer diagnosis models

Improved accuracy over traditional methods

Potential for democratizing access to diagnostics

Abstract

This research presents an innovative approach to cancer diagnosis and prediction using explainable Artificial Intelligence (XAI) and deep learning techniques. With cancer causing nearly 10 million deaths globally in 2020, early and accurate diagnosis is crucial. Traditional methods often face challenges in cost, accuracy, and efficiency. Our study develops an AI model that provides precise outcomes and clear insights into its decision-making process, addressing the "black box" problem of deep learning models. By employing XAI techniques, we enhance interpretability and transparency, building trust among healthcare professionals and patients. Our approach leverages neural networks to analyse extensive datasets, identifying patterns for cancer detection. This model has the potential to revolutionise diagnosis by improving accuracy, accessibility, and clarity in medical decision-making, possibly leading to earlier detection and more personalised treatment strategies. Furthermore, it could democratise access to high-quality diagnostics, particularly in resource-limited settings, contributing to global health equity. The model's applications extend beyond cancer diagnosis, potentially transforming various aspects of medical decision-making and saving millions of lives worldwide.