ClarifAI - Detecting Breast Cancer with AI

Abstract

Breast cancer is one of the most commonly diagnosed cancers worldwide and a leading cause of death, especially among women. The lack of early detection, negatively impacts survival rates, as treatment is more effective in the early stages of the disease. This research project investigates the use of artificial intelligence, specifically machine learning, for the early detection of breast cancer using mammogram imaging. By training a network model on a dataset of labeled roughly 1,000 mammogram images, titled normal, abnormal, cancerous, and benign, the goal was to develop an AI model capable of distinguishing between cancerous and non-cancerous mammogram scans. Initial model accuracy was around 25%, indicating random prediction levels. However, after refining the model and adjusting the training process, accuracy increased to approximately 37%, demonstrating the model’s ability to learn and improve. Although this performance is not yet suitable for clinical use, the results confirm the model’s potential and offer a foundation for future improvements. Challenges such as overfitting, data bias, and limited data availability, were explored as their impacts were shown during the research process. The findings support the role of AI as a valuable tool in advancing medical diagnostics and improving patient outcomes through earlier breast cancer detection.