Makine öğrenmeyi kullanarak ince iğne aspirasyon görüntülerinde meme kanseri tahmini

Breast cancer, with an estimated 1.5 million new cases per year, is a critical worldwide health problem owing to the disease's high fatality rate. It is also the most common cancer among women, accounting for 16% of all cancer cases in this demographic globally. Mammography is the most successful way for detecting breast cancer in its early stages, allowing for early intervention. Breast cancer may be detected in its early stages using ultrasound, magnetic resonance imaging, and computational tomography, albeit these methods are not as good in investigating and diagnosing these abnormalities as other ways This work aims to develop a methodology that is capable of identifying and classifying cancerous tumors using digital mammography images. In this way, the diagnostic work of these images, carried out by radiologist specialists, will be supported by the system to be developed through the methodology presented in this work. Therefore, with the proposed methodology, it was expected an increase in the agility and effectiveness of the diagnosis and, thus, to increase the chances of cure of the patients, since a rapid and effective diagnosis is of fundamental importance for the treatment of the disease.