Harnessing Convolutional Neural Networks for Histopathological Breast Cancer Classification.

Abstract

Recent advancements in Convolutional Neural Networks (CNNs) have significantly supported the field of breast cancer discovery using medical imaging. An improvised DenseNet architecture for the classification of histo-pathological breast cancer images is explored in this work. Leveraging the effectiveness of DenseNet in capturing intricate patterns through dense connectivity, our improvised architecture aims achieve high accuracy and efficiency of classification. The model integrates novel features such as optimized bottleneck layers and attention mechanisms, contributing to improved feature extraction and classification capabilities. The improvised DenseNet produced a accuracy of 93.39% on the breakhis dataset. A summary of key findings and future research directions, emphasizing the need of custom CNN models in breast cancer detection is provided.