Early breast cancer diagnosis is important in enhancing survival rates of women with breast cancer which is one of the highest causes of cancer mortality in the world. The traditional diagnostic methods like mammography and ultrasound are very dependent on the interpretation of radiologists and have varying results, thus resulted into delayed or inaccurate diagnosis. New developments in the field of artificial intelligence (AI) are effective solutions that help increase the accuracy and consistency of diagnostics. This research paper presents a machine learning model that will be used in detecting and classifying breast cancer based on medical imaging data through the use of a convolutional neural network (CNN). The model was trained using a wide range of data (mammograms, ultrasound images and scans of magnetic resonance imaging) to differentiate between benign and malignant lesions with great accuracy. The suggested technique proves better diagnostic capability than the usual systems and minimizes the human error and clinical burden. These results suggest the possibility of AI-aided medical imaging to improve the effectiveness of the system in the detection of breast cancers at the initial stages and patient outcomes.
Yadav et al. (Thu,) studied this question.