Classification of Histopathological Images of Breast Cancer Tissues into Benign Subcategories and Malignant Subcategories

Breast cancer stands as a leading and dangerous illness which affects people throughout the world. Correct diagnosis of breast cancer through histopathological image classification serves as a fundamental step for developing successful treatment strategies. The research introduces a reliable deep-learning system which detects breast cancer tissues between benign and malignant categories for enhanced medical diagnostic precision. The designed model implements state-of-the-art image analytics methods with deep neural networks to obtain valuable information from histopathological pictures. Our techniques (ResNet 50, Inception V3) display better classification accuracy and computing speed in contrast to traditional methods according to comparative assessments. The research findings advance automated histopathological analysis by enabling pathologists to make better clinical decisions through their assistance