Improved graph neural network-based green anaconda optimization for segmenting and classifying the lung cancer

Abstract

<abstract> <p>Normal lung cells incur genetic damage over time, which causes unchecked cell growth and ultimately leads to lung cancer. Nearly 85% of lung cancer cases are caused by smoking, but there exists factual evidence that beta-carotene supplements and arsenic in water may raise the risk of developing the illness. Asbestos, polycyclic aromatic hydrocarbons, arsenic, radon gas, nickel, chromium and hereditary factors represent various lung cancer-causing agents. Therefore, deep learning approaches are employed to quicken the crucial procedure of diagnosing lung cancer. The effectiveness of these methods has increased when used to examine cancer histopathology slides. Initially, the data is gathered from the standard benchmark dataset. Further, the pre-processing of the collected images is accomplished using the Gabor filter method. The segmentation of these pre-processed images is done through the modified expectation maximization (MEM) algorithm method. Next, using the histogram of oriented gradient (HOG) scheme, the features are extracted from these segmented images. Finally, the classification of lung cancer is performed by the improved graph neural network (IGNN), where the parameter optimization of graph neural network (GNN) is done by the green anaconda optimization (GAO) algorithm in order to derive the accuracy maximization as the major objective function. This IGNN classifies lung cancer into normal, adeno carcinoma and squamous cell carcinoma as the final output. On comparison with existing methods with respect to distinct performance measures, the simulation findings reveal the betterment of the introduced method.</p> </abstract>

Advancing Oncology Diagnostics: AI-Enabled Early Detection of Lung Cancer Through Hybrid Histological Image Analysis

“…This high accuracy was attained by using GWO for FE, IWO for feature selection, and the RAdam approach for hyperparameter tuning. Krishnan et al [26] introduced an improved graph neural network (IGNN) optimized by the green anaconda optimization (GAO) algorithm to maximize accuracy in segmenting and classifying LC. The process involved pre-processing images using the gabor filter method, segmentation with the modified expectation maximization (MEM) algorithm, and FE through the histogram of oriented gradient (HOG) scheme.…”

“…Not provided [26] LC25000 Histogram of oriented gradient (HOG) + hyperparameter tuning green anaconda optimization (GAO) + improved graph neural network (IGNN) accuracy of 98.9%.…”