Main Article Content
Breast cancer is one of the most common types of cancer in women. The likelihood of rescuing a breast cancer patient is highly reliant on early identification and treatment commencement. The high death rate due to breast cancer is attributable to a lack of knowledge of the necessity of early detection of symptoms of various breast cancers and a lack of training in breast cancer symptom identification. A computer-aided diagnostic (CAD) expert system facilitates a pathologist for early breast cancer diagnosis and determines if the tumour is harmless or aggressive. The goal is to use breast records to examine the influence of CAD systems. This study included the evaluation of present state-of-the-art methodologies implemented for every phase, including traditional procedures, comparisons within approaches, and technical specifications with advantages and limitations. Finally, the research gaps in existing machine learning methodologies for implementation and recommendations for future researchers are described.