Classifying Lung and Breast Tumor Prediction Using Support Vector Machine and Deep Convolutional Neural Network

Author(s):

Aswitha S*, Shanmuganathan C, Yogesh S and Krithick I

Cancer remains a global health challenge, demanding innovative solutions for early detection and precise classification. This project presents a multistage approach for tumor classification and detection in the context of classifying between benign and malignant cancer. Our use case is to take breast and lung cancer for deploying our model to predict whether they have benign or malignant tumor. The system leverages SVM machine learning and CNN deep learning technique to provide accurate and actionable insights for medical practitioners. The first stage focuses on breast tumor classification using Support Vector Machines (SVM) based on key tumor biomarkers, including 'mean radius,' 'mean texture,' 'mean perimeter,' 'mean area,' and 'mean smoothness.' This initial classification helps identify malignant cases, prompting further evaluation. In the second stage, Convolutional Neural Networks (CNN) with YOLO (You Only Look Once) are employed for the real-time detection of lung tumors. This stage is triggered when a malignant breast tumor is detected, enabling prompt lung cancer assessment. The final stage addresses the subtyping of malignant lung tumors, categorizing them as adenocarcinoma, large cell carcinoma, or squamous cell carcinoma. This comprehensive approach aids in providing tailored treatment recommendations, enhancing patient care. The project underscores the significance of accurate tumor classification and early detection, which are pivotal in improving patient outcomes and streamlining clinical decision-making.