Enhancing Early Detection of Lung Cancer through Advanced Image Processing Techniques and Deep Learning Architectures for CT Scans  

作　　者：Nahed Tawfik Heba M.Emara Walid El-Shafai Naglaa F.Soliman Abeer D.Algarni Fathi EAbd El-Samie 

机构地区：[1]Computers and Systems Department,Electronics Research Institute,El Nozha,Huckstep Cairo,12622,Egypt [2]Department of Electronics and Electrical Communications Engineering,Faculty of Electronic Engineering,Menoufia University,Menouf,32952,Egypt [3]Security Engineering Lab,Computer Science Department,Prince Sultan University,Riyadh,11586,Saudi Arabia [4]Department of Information Technology,College of Computer and Information Sciences,Princess Nourah bint Abdulrahman University,Riyadh,84428,Saudi Arabia

出　　处：《Computers, Materials & Continua》2024年第10期271-307,共37页计算机、材料和连续体（英文）

基　　金：the Deanship of Scientific Research at Princess Nourah bint Abdulrahman University,through the Research Groups Program Grant number RGP-1444-0054.

摘　　要：Lung cancer remains a major concern in modern oncology due to its high mortality rates and multifaceted origins,including hereditary factors and various clinical changes.It stands as the deadliest type of cancer and a significant cause of cancer-related deaths globally.Early diagnosis enables healthcare providers to administer appropriate treatment measures promptly and accurately,leading to improved prognosis and higher survival rates.The significant increase in both the incidence and mortality rates of lung cancer,particularly its ranking as the second most prevalent cancer among women worldwide,underscores the need for comprehensive research into efficient screening methods.Advances in diagnostic techniques,particularly the use of computed tomography(CT)scans,have revolutionized the identification of lung cancer.CT scans are renowned for their ability to provide high-resolution images and are particularly effective in detecting small,calcified areas,crucial for identifying earlystage lung cancer.Consequently,there is growing interest in enhancing computer-aided detection(CAD)systems.These algorithms assist radiologists by reducing false-positive interpretations and improving the accuracy of early cancer diagnosis.This study aims to enhance the effectiveness of CAD systems through various methods.Initially,the Contrast Limited Adaptive Histogram Equalization(CLAHE)algorithm is employed to preprocess CT scan images,thereby improving their visual quality.Further refinement is achieved by integrating different optimization strategies with the CLAHE method.The CutMix data augmentation technique is applied to boost the performance of the proposed model.A comparative analysis is conducted using deep learning architectures such as InceptionV3,ResNet101,Xception,and EfficientNet.The study evaluates the performance of these architectures in image classification tasks,both with and without the implementation of the CLAHE algorithm.The empirical findings of the study demonstrate a significant reduction in the false positi

关 键 词：Lung cancer detection CLAHE algorithm optimization deep learning CLASSIFICATION feature extraction healthcare applications 

分 类 号：R73[医药卫生—肿瘤] TP391.41[医药卫生—临床医学]