Hybrid Deep Learning and Machine Learning Approaches for Enhanced Breast Cancer Detection and Early Diagnosis

Abstract

Breast cancer remains one of the most critical health challenges worldwide, necessitating advancements in early detection to improve patient survival rates. This study aims to analyze the effectiveness of machine learning and swarm intelligence techniques in breast cancer detection, and to develop a multilayer deep learning model for accurate symptom identification. A cascaded model based on deep learning is proposed for enhancing the detection of early-stage breast cancer, which is vital for reducing invasive treatments like chemotherapy and surgery. The research also compares the performance of the proposed algorithm with existing techniques to demonstrate its superior accuracy and diagnostic efficiency.

The study introduces a Convolutional Neural Network (CNN) model for detecting invasive ductal carcinoma (IDC) in whole-slide images (WSIs), achieving 87% accuracy. By leveraging pre-stage data processing and a Recurrent Neural Network (RNN) as a meta classifier, the proposed model further enhances breast cancer detection, reaching an impressive validation accuracy of 98.08%. Despite these promising results, the research highlights the limitations of relying on secondary datasets and calls for the inclusion of primary data in future studies. Incorporating gene sequence data and attention mechanisms could provide more comprehensive predictions and improve classification accuracy. Furthermore, the study proposes a hybrid classifier combining CNN and Long Short-Term Memory (LSTM) networks, with future work focused on applying the model to various breast cancer types and investigating hardware implementation for real-time diagnostics.