Detection of Lung Cancer Using Multi-Stage Image Processing and Advanced Deep Learning InceptiMultiLayer-Net Model

Abstract

This study aims to improve early lung cancer detection by creating a sophisticated Computer-Aided Diagnosis (CAD) system. This system employs advanced image processing techniques such as adaptive dynamic histogram equalization (ADHE), Local Binary Pattern (LBP), and Tsallis thresholding to effectively reduce noise, analyze textures, and segment regions. It also includes the InceptiMultiLayer-Net (IML-Net), an advanced version of the Inception V3 architecture designed to capture complex features in medical images. The IML-Net includes a multiclass Error-Correcting Output Codes (ECOC) Support Vector Machine (SVM) classifier, which improves the system’s ability to handle complex classification tasks. The system also employs statistical features such as mean, variance, energy, entropy, and correlation to fully describe the characteristics of segmented regions. With an impressive 99.573% accuracy in identifying lung cancer-affected regions, as well as a sensitivity of 99.46% and a specificity of 99.24%, this CAD system has significant potential as an early lung cancer detection tool. These findings highlight the system’s ability to assist clinicians in making accurate diagnoses, ultimately improving patient outcomes significantly.