Breast cancer is one of the leading causes of mortality among women worldwide, and early detection is crucial for improving survival rates. Computer vision-based medical image processing, combined with machine learning techniques, has emerged as a powerful approach for enhancing the accuracy and efficiency of breast cancer prediction. This paper presents a comprehensive study on using computer vision methods to analyze mammograms and other breast imaging modalities, such as ultrasound and MRI, for early detection and diagnosis of breast cancer and to detect the initial phase tumors which shall not be prone to human error using image processing techniques such as image preprocessing, image segmentation, features extraction and selection and image classification. We explore various machine learning algorithms, including traditional models like Support Vector Machines (SVM) and Random Forest, as well as more advanced deep learning models like Convolutional Neural Networks (CNNs), to automatically identify and classify malignant and benign tumors. Our approach leverages feature extraction, image segmentation, and classification techniques to improve the accuracy of predictions. The proposed system is validated on publicly available datasets, demonstrating its effectiveness in achieving high sensitivity and specificity. This work contributes to the field of medical imaging by providing a robust framework for breast cancer prediction, potentially aiding radiologists in decision-making and enhancing patient outcomes.

Breast cancer is one of the leading causes of mortality among women worldwide, and early detection is crucial for improving survival rates.