Quantum Optimized AlexNet for Histopathology Breast Image Diagnosis

Breast cancer is a prevalent and life-threatening disease affecting millions of women worldwide. Timely and accurate detection is crucial for improving patient outcomes. Deep learning techniques have shown remarkable success in image classification tasks, including breast cancer diagnosis. However, the integration of quantum computing into deep learning frameworks remains relatively unexplored. This paper investigates the potential of leveraging quantum computing to enhance image classification, particularly in breast cancer detection. The focus is on utilizing the "breakhis-400x" binary dataset to develop an advanced breast cancer image classifier. The proposed Quantum-Optimized AlexNet (QOA) approach, combines the feature extraction capabilities of the AlexNet model with a quantum layer acting as a linear layer. Experimental results on the BreakHis-400x dataset demonstrate the significant potential of the QOA model, achieving an overall accuracy of 93.67%. These findings highlight the utility of Quantum Computing in improving deep learning models for image classification, particularly in medical imaging analysis, and contribute to the advancement of precision medicine in breast cancer diagnosis.