Breast Cancer Diagnosis with XAI-Integrated Deep Learning Approach

Breast cancer is one of the world's significant health challenges. There is a need to have better techniques in the early diagnosis of this disease to increase patients' survival rates. This paper introduces a robust approach for breast cancer detection by integrating deep-learning and classical machine-learning techniques into a custom lightweight neural network model. This approach is based on integrating conventional machine-learning and deep-learning paradigms using a range of data pre-processing steps, including data cleaning, label converting, scaling, and feature selecting to improve the given dataset's readiness for training. The proposed model demonstrated impressive accuracy in breast cancer detection compared to individual classifiers by achieving an overall accuracy of 97.54 %. Additionally, integrating eXplainable Artificial Intelligence (XAI) techniques gives the application interpretability and transparency for clinicians to make sense of the feature's importance and individual prognosis. It is a more accurate and easy-to-understand tool for clinicians, making it a better use of faulty or confusing reference values. This study presents the need to learn more about making deep learning and eXplainable Artificial Intelligence (XAI) complementary approaches to breast cancer diagnosis and treatment research.