(untitled) (REMUNOM, 2024)
This work analyzes the use of Machine Learning (ML) in the early diagnosis of breast cancer, focusing on its application in imaging exams. The issue lies in the need to improve the accuracy and efficiency of diagnoses, as breast cancer, being one of the leading causes of death among women, requires early interventions to increase survival rates. The central objective of this study is to evaluate the effectiveness of ML algorithms in detecting subtle patterns in mammographic images, overcoming the limitations of traditional methods, such as mammography, which have high false-positive rates. To achieve this objective, a literature review was conducted, including the analysis of scientific articles and relevant studies in the Google Scholar database. The research highlighted that techniques such as Convolutional Neural Networks (CNNs) demonstrate superior ability in identifying malignant lesions with greater accuracy. The results show that the application of ML can transform clinical practice, enabling more efficient screening and faster diagnoses.