earticle

영어

Breast cancer remains the foremost cause of cancer-related mortality worldwide. The histopathological diagnosis is impeded by the intricate nature of image interpretation and the presence of inter-observer variability among pathologists. Deep learning (DL) for cancer image understanding has revolutionized accurate breast cancer diagnosis, marking a significant advancement in medical image analysis. Researchers proposed DL-based intelligent models to overcome the challenges of manual observations. However, the existing models suffer from a considerable computational burden, demanding substantial time investments that restrict efficient and scalable breast cancer diagnosis solutions. Our study introduces an automated breast cancer diagnosis system employing a lightweight Convolutional Neural Network (CNN) model, adept at extracting intricate features from histopathological images. Our system has attained superior accuracy through extensive experimentation on a comprehensive breast cancer dataset while employing fewer parameters compared to state-of-theart (SOTA) techniques.