Improved Model for Genetic Algorithm-Based Accurate Lung Cancer Segmentation and Classification

作     者：K.Jagadeesh A.Rajendran 

作者机构：Department of Electronics and Communication EngineeringSNS College of EngineeringCoimbatoreTamilnaduIndia Department of Electronics and Communication EngineeringKarpagam College of EngineeringCoimbatoreTamilnaduIndia 

出 版 物：《Computer Systems Science & Engineering》 (计算机系统科学与工程（英文）)

年 卷 期：2023年第45卷第5期

页      面：2017-2032页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：Cancer diagnosis segmentation enhancement histogram equalization probabilistic rate neural networks(PNN) classification 

摘      要：Lung Cancer is one of the hazardous diseases that have to be detected in earlier stages for providing better treatment and clinical support to *** lung cancer diagnosis,the computed tomography(CT)scan images are to be processed with image processing techniques and effective classification process is required for appropriate cancer *** present scenario of medical data processing,the cancer detection process is very time consuming and *** that,this paper develops an improved model for lung cancer segmentation and classification using genetic *** the model,the input CT images are pre-processed with the filters called adaptive median filter and average *** filtered images are enhanced with histogram equalization and the ROI(Regions of Interest)cancer tissues are segmented using Guaranteed Convergence Particle Swarm Optimization *** classification of images,Probabilistic Neural Networks(PNN)based classification is *** experimentation is carried out by simulating the model in MATLAB,with the input CT lung images LIDC-IDRI(Lung Image Database Consortium-Image Database Resource Initiative)benchmark *** results ensure that the proposed model outperforms existing methods with accurate classification results with minimal processing time.