ML-DRIVEN APPROACH FOR BREAST CANCER CLASSIFICATION FROM MAMMOGRAPHIC IMAGES
Abstract
Breast cancer is one of the most prevalent and life-threatening diseases among women worldwide. Early detection plays a crucial role in improving survival rates. Mammographic imaging is a widely used screening tool for breast cancer detection.In the existing system of breast cancer detection primarily rely on manual interpretation by radiologists, which can be time-consuming and subjective. While some computer-aided diagnosis (CAD) systems exist, they often lack the accuracy and robustness required for clinical use.The existing systems for breast cancer diagnosis suffer from limitations such as manual interpretation, low accuracy, and dependency on human expertise. There is a need for a more accurate and efficient approach that can automatically classify mammographic images with high precision,aiding in early detection and reducing the workload of radiologists. Our proposed method utilizes a machine learning approach, specifically the Random Forest Classifier (RFC), to classify mammographic images into benign and malignant categories. We preprocess the images to extract relevant features, such as texture, shape, and intensity, and then train the RFC model on these features to accurately classify the images, the system can aid in the early detection of breast cancer, leading to better treatment outcomes.
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