Ettore Rocchi, Sara Peluso, Stefano Amatori, Davide Sisti
New indexes for myofibril linearity in muscle image analysis
European journal of translational myology
lung cancer remains leading cause cancerrelated mortality globally prognosis significantly dependent earlystage detection traditional diagnostic methods though effective often face challenges regarding accuracy early detection scalability invasive timeconsuming prone ambiguous interpretations study proposes advanced machine learning model designed enhance lung cancer stage classification using ct scan images aiming overcome limitations offering faster noninvasive reliable diagnostic tool utilizing iqothnccd lung cancer dataset comprising ct scans various stages lung cancer healthy individuals performed extensive preprocessing including resizing normalization gaussian blurring convolutional neural network cnn trained preprocessed data class imbalance addressed using synthetic minority oversampling technique smote models performance evaluated metrics accuracy precision recall f1score roc curve analysis results demonstrated classification accuracy 9964 precision recall f1score values exceeding 98 across categories smote significantly enhanced models ability classify underrepresented classes contributing robustness diagnostic tool findings underscore potential machine learning transforming lung cancer diagnostics providing high accuracy stage classification could facilitate early detection tailored treatment strategies ultimately improving patient outcomes