Diagnostic Strategies for Breast Cancer Detection: From Image Generation to Classification Strategies Using Artificial Intelligence Algorithms

Simple Summary With the recent advances in the field of artificial intelligence, it has been possible to develop robust and accurate methodologies that can deliver noticeable results in different health- related areas, where the oncology is one the hottest research areas nowadays, as it is now possible to fuse information that the images have with the patient medical records in order to offer a more accurate diagnosis. In this sense, understanding the process of how an AI-based methodology is developed can offer a helpful insight to develop such methodologies. In this review, we comprehensively guide the reader on the steps required to develop such methodology, starting from the image formation to its processing and interpretation using a wide variety of methods; further, some techniques that can be used in the next-generation diagnostic strategies are also presented. We believe this helpful insight will provide deeper comprehension to students and researchers in the related areas, of the advantages and disadvantages of every method. Breast cancer is one the main death causes for women worldwide, as 16% of the diagnosed malignant lesions worldwide are its consequence. In this sense, it is of paramount importance to diagnose these lesions in the earliest stage possible, in order to have the highest chances of survival. While there are several works that present selected topics in this area, none of them present a complete panorama, that is, from the image generation to its interpretation. This work presents a comprehensive state-of-the-art review of the image generation and processing techniques to detect Breast Cancer, where potential candidates for the image generation and processing are presented and discussed. Novel methodologies should consider the adroit integration of artificial intelligence-concepts and the categorical data to generate modern alternatives that can have the accuracy, precision and reliability expected to mitigate the misclassifications.