Enhancing Lung Cancer Diagnosis: A Classification Based Machine Learning approach

Abstract: Out of all types of cancer in the world, lung cancer is one of the biggest killers and is classified as either non-small cell (NSCLC) or small cell (SCLC). Making sure lung cancer is properly staged is necessary for making a good treatment plan and for understanding prognosis. Most lung cancer staging today involves seeing and reading CT images by hand which requires much time, may lead to errors and strongly depends on how skilled the radiologist is. Such limitations frequently mean that patients are diagnosed later and treated in suboptimal ways. The project here suggests an innovative framework to automatically detect and stage lung cancer. Radiomics is used in the system to measure features like intensity, shape and texture featured inside a tumor from CT scans. PCA is introduced to lighten the computational stress by selecting and maintaining the most important features. Input from the reduced features feeds a custom neural network, the TNMClassifier which is developed to categorize Tumor (T), Node (N) and Metastasis (M) stages using the TNM staging system. The design employs fully connected layers along with dropout layers, so it isn’t likely to overfit and performs well. Classifying the TNM stages of lung cancer is accurate at 98% with the proposed system which helps address the weaknesses of existing solutions. This new method may help simplify work for clinicians, reduce chances of wrong diagnoses and provide better patient results