Reliable Lung Nodule Image Classification Using Texture Based Convolutional Neural Network

Lung nodules are important markers of lung cancer, and prompt treatment makes early discovery much more likely to improve patient survival. Computer-Aided Diagnosis (CAD) systems were developed because radiologists find it difficult and time-consuming to classify malignant nodules in Computed Tomography (CT) images. Deep learning developments have consistently enhanced CAD's ability to screen for lung cancer. To improve the accuracy of pulmonary nodule classification, we use a Transferable Texture-Based Convolutional Neural Network (CNN) in this work. In order to maximize feature representation, our model integrates an Energy Layer (EL) to extract texture-based features from the convolutional layer. To guarantee strong classification performance, the suggested method is assessed using importantperformance metrics as accuracy, sensitivity, specificity, F1-score, and AUC-ROC.