Quantum Neural Network Approach for Early Detection of Breast Cancer
Dinesh Kumar G, N Varatharajan, Dr. M. Tamilselvi +3 more
Breast cancer is one among the most common diseases that are encountered in the world and one of the principal causes of death among women, early detection being crucial to enhance survival rates but mammograms are an important tool that can save lives by finding breast cancer at its earliest stages but the manual evaluation is dependent on a radiologist's clear interpretation and analysis of which sometimes erroneously or misses the key findings. This research work deals with breast cancer detection from mammogram images and hence provides a solution for the issues identified by training Quantum Convolutional Neural Network (Quantum CNN). Quantum CNNs is recently being integrated with classical deep learning models in the form of Quantum CNN where quantum mechanical properties are used to support feature extraction and data handling. This research work trains the Quantum CNN using publicly available datasets, including the CBIS-DDSM Breast Cancer Image Dataset and the Breast Histopathology Images dataset. Key methods used include data preprocessing techniques such as Region of Interest (ROI) extraction, morphological operations, and quantum-enhanced convolutional layers for image classification. The proposed model demonstrates improved accuracy and computational efficiency over classical CNNs, making it a promising tool for more accurate and faster breast cancer detection.
IEEE Global Conference for Advancement in Technology (GCAT)•Oct 24, 2025•0 citations