Breast Cancer Detection

Breast cancer is a leading cause of mortality among women worldwide, highlighting the critical need for early and accurate detection. Conventional diagnostic approaches, such as mammography and biopsy, are resource-intensive and susceptible to human error. This paper presents creation of an automated system for classifying breast cancer utilizing deep learning techniques on ultrasound pictures of the breasts. The proposed system employs VGG16 and Xception convolutional neural network architectures to classify breast lesions as benign, malignant, or normal, leveraging their strong feature extraction capabilities. The models that have been trained are included in a user-friendly web application built with the Flask framework, enabling healthcare professionals to upload ultrasound images and receive real-time diagnostic predictions. The system incorporates two user roles: Admin, responsible for user management and FAQ maintenance, and User, who can register, upload images, and access guidance resources. Experimental results demonstrate that the AI-driven approach improves diagnostic accuracy and significantly reduces assessment time, supporting rapid clinical decision-making. Keywords: VGG16, Exception convolutional neural network, Convolutional Neural Networks (CNNs), Breast cancer, Deep Learning.