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Background: Breast cancer is the second leading cause of death of females worldwide. Mammograms are useful in early cancer diagnosis as well when the patient can sense symptoms or become observable. Inspection of mammograms in search of breast tumors is a difficult task that radiologists must carry out frequently. Objective: This paper provides a summary of possible strategies used in automated systems for a mammogram, especially focused on segmentation techniques used for cancer localization in mammograms. Methods: This article is intended to present a brief overview of nonexperts and beginners in this field. It starts with an overview of the mammograms, public and private available datasets, image processing techniques used for a mammogram, cancer classification followed by cancer segmentation using the machine, and deep learning techniques. Conclusion: The approaches used in these stages are summarized and their advantages and disadvantages with possible future research directions are discussed. In the future, we will train a model on medical images that can be used for mammograms and other medical images.

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