A Review on Deep Learning Based Breast Cancer Classification for Histopathology Images

Breast cancer is one of the most common and life-threatening cancers in women worldwide. The clinical gold standard for diagnosis is still histopathological examination, but it is time-consuming, subject to human expertise, and susceptible to human error. This project presents a deep learning system based on DenseNet201 architecture for automated and enhanced accuracy of breast cancer detection from histopathology images. The system is developed using the BreaKHis dataset, employing state-of-the-art preprocessing and data augmentation methods for enhancing robustness. Performance metrics such as accuracy, precision, recall, and AUC-ROC results reflect the system's performance as a sound diagnostic tool in clinical settings. Histopathological diagnosis, while critical, entails a number of challenges including inter-observer variability, workload burden on pathologists, and risk of delayed treatment decisions. Convolutional Neural Networks (CNNs), specifically DenseNet201, have proven to be useful tools for extracting complex visual patterns in medical images. In this research, transfer learning, reuse of features, and a well- designed classification pipeline are utilized to separate benign from malignant samples successfully. The application of artificial intelligence to pathology is not just a means of improving diagnostic correctness but also of broadening access to healthcare through making sound diagnostics available in low-resource environments. By providing a speedy and reproducible second opinion, the model described here is an advance toward real-time, AI-augmented cancer diagnosis that can revolutionize conventional clinical practice.