Breast Cancer Detection and Prediction using Image Processing and ML

Abstract

Abstract: Breast cancer is one of the most common and potentially life-threatening forms of cancer that affects a significant number of women worldwide. Early detection of breast cancer plays a crucial role in improving patient outcomes and survival rates. Machine learning algorithms have the potential to analyze large volumes of medical imaging data, extract meaningful features, and assist in the identification of suspicious regions or potential tumors. By leveraging these algorithms, healthcare professionals can make more accurate and timely diagnoses, leading to improved patient care and outcomes. Breast cancer is a prevalent form of cancer that affects a significant number of women worldwide. Early detection plays a crucial role in improving patient outcomes and survival rates. Breast cancer detection refers to the process of identifying abnormal changes in breast tissue, such as tumors or growths, that may indicate the presence of cancer cells. Early detection plays a vital role in improving patient outcomes by allowing for timely intervention and targeted treatment plans. Several techniques are utilized in breast cancer detection, including screening mammography, clinical breast examination, and breast self-examination. Mammography, the most common method, involves using low-dose X-rays to capture images of the breast tissue. It can detect tumors or suspicious areas even before they can be felt by a physician or the patient. In recent years, advancements in medical imaging and machine learning techniques have shown promising results in breast cancer detection. The designing of the model began with classification of Histopathological image dataset into Cancerous and Non - cancerous classes using Support Vector Machine (SVM) and Convolutional Neural Network (CNN) algorithms. Both the classifiers are examined on the basis of sensitivity, specificity..