Modified convolutional neural network for lung cancer detection: Improved cat swarm-based optimal training

Abstract

Lung cancer is the most lethal and severe illness in existence. However, lung cancer patients may live longer if they receive early detection and treatment. In the medical field, the best imaging technique is CT scan imaging as it is more complex for doctors to identify cancer and interpret from CT scan images. Consequently, the computer-aided diagnosis (CAD) is more useful for doctors to find out cancerous nodules. To identify lung cancer, a number of CAD techniques utilising machine learning (ML) and image processing are used nowadays. The goal of this study is to present a novel method for detecting lung cancer that entails four main steps: (i) Pre-processing, (ii) Segmentation, (iii) Feature extraction, and (iv) Classification. ”The input image is first put through a pre-processing step in which the CLAHE model is used to pre-process the image. The segmentation phase of the pre-processed images is then initiated, and it makes use of a modified Level set segmentation method. The retrieved features from the segmented images include statistical features, colour features, and texture features (GLCM, GLRM, and LBP). The Layer Fused Conventional Neural Network (LF-CNN) is then utilised to classify these features in the end. Particularly, layer-wise modification is carried out, and along with that, the LF-CNN is trained by the Modified Cat swarm Optimization (MCSO) Algorithm via selecting optimal weights. The accepted scheme is then compared to the current models in terms of several metrics, including recall, FNR, MCC, FDR, Threat score, FPR, precision, FOR, accuracy, specificity, NPV, FMS, and sensitivity.