Tri-level lung cancer classification via deep learning based GoogleNet with computed tomography images

Abstract

Lung cancer (LC) is one of the most prevalent causes of cancer-related death worldwide. World Health Organization (WHO) classifies LC into two broad histological subtypes: non-small cell lung cancer (NSCLC) which is the cause of about 85% of cases and small cell lung cancer (SCLC) which makes up the remaining 15%. Several issues can influence LC detection including poor image quality, insufficient training data, low-quality image characteristics, and poor tumor localization. To overcome these challenges a novel TRI-level LC classification via deep learning-based GoogleNet with computed tomography (CT) images (TRI-LCNet) approach has been proposed for early-stage LC detection using CT images. Initially, the LC-input images CT are collected from openly accessible datasets. The lung CT images have been preprocessed using a Gaussian star filter (GaSF) to decrease noise, followed by feature extraction using GoogleNet. The extracted LC features are then given into a support vector machine (SVM) which is utilized as a classification tool to distinguish between different classes of LC cases. The TRI-LCNet approach performance was assessed by several metrics: specificity, accuracy, F1 score, and recall. The outcomes show that the suggested method obtains a higher accuracy range of 96.93% for the early identification of LC.