International Research Journal of Education and Technology

Breast cancer is one of the most prevalent and potentially fatal forms of cancer affecting women globally. Early and accurate diagnosis plays a crucial role in improving patient outcomes and survival rates. Deep learning techniques have demonstrated remarkable success in various medical applications, including breast cancer diagnosis. These abstract highlights recent advancements and applications of deep learning methods in the field of breast cancer diagnostics. This paper reviews the utilization of deep learning algorithms for breast cancer diagnosis from medical imaging modalities such as mammograms, ultrasounds, and MRIs. CNNs (Convolutional neural networks) have proven particularly effective in extracting intricate patterns and features from medical images, enabling the differentiation between malignant and benign breast lesions. Transfer learning, a technique where pre-trained models are fine-tuned for specific tasks, has significantly accelerated the development of accurate diagnostic tools.

Keywords: Breast cancer ,Deep learning, Convolution neural networks, computer-aided detection,