Published Paper Details:

With the use of artificial intelligence (AI) tools, notably machine learning algorithms, breast cancer diagnosis has advanced significantly. This study examines many approaches used in the diagnosis of breast cancer, with a particular emphasis on the use of convolutional neural networks (CNNs) for the analysis of mammography pictures. To create strong artificial intelligence (AI) models that can recognize minute anomalies suggestive of breast cancer, a series of steps including data collecting, pre-processing, training, validation, and testing are required. Early diagnosis, increased accuracy, increased speed and efficiency, tailored medication, support for radiologists, resource optimization, ongoing learning, and easier access to healthcare are among the goals of using AI in breast cancer detection. Healthcare professionals may enhance patient outcomes by using AI-powered technologies to speed the interpretation process, customize treatment regimens, and allocate resources optimally.