MAMMOGRAM DETECTION USING MACHINE LEARNING

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Mammogram detection plays a critical role in early breast cancer diagnosis, and leveraging machine learning techniques can significantly enhance diagnostic accuracy. This project addresses the classification and segmentation of mammogram images to identify abnormalities. We employed a Convolutional Neural Network (CNN) algorithm for binary classification to categorize mammogram images as either ‘Normal’ or ‘Malignant’. For region-based segmentation of detected abnormalities, we utilized an Attention-based optimized UNET algorithm. The preprocessing phase included resizing, normalization, and shuffling of the dataset to prepare it for training. Due to the large size of comprehensive mask image datasets (166 GB), which are impractical to download or process with standard systems, we used a smaller subset of images for training. Consequently, while the classification model performs with reasonable accuracy, the segmentation results may exhibit limited precision due to the constrained training data. The trained model can be applied to test images, where malignant predictions trigger segmentation of the affected regions using the UNET algorithm. This approach demonstrates the feasibility of integrating CNN and UNET for mammogram analysis, though future work will benefit from access to larger datasets for improved segmentation accuracy.