Analysis of breast cancer classification and segmentation techniques: A comprehensive review

Abstract

Breast cancer (BC) is caused by the mutation of breast cells and their uncontrolled proliferation, making diagnosis critical at the chronic stage. Early cancer detection can help plan treatment and reduce its severity and mortality rate. It can be confirmed by the biopsy test. Due to technological advancements, it can be effectively detected by various modalities, such as X-rays, ultrasound, MRI scans, histopathology images, etc. Development in machine learning (ML), data mining, sensors, and signal processing techniques gained popularity in early breast cancer detection and grading. However, these techniques must be improved for better prediction, localization, and grading of cancer tissues. This manuscript discusses the tissue variation due to the propagation of cancer and its havoc in life, along with various AI-based techniques for early identification with their limitations. Publicly available breast cancer databases and performance evaluation metrics used by the researchers have been summarized. Based on the limitations and potential strengths of various techniques, a deep learning (DL) model for multi-class classification of breast cancer for the whole slide image (WSI) is proposed. This study identifies ongoing issues essential for driving future advancements in BC detection and segmentation to improve clinical outcomes.