An Effective Lung Cancer Diagnosis Model Using Pre-Trained CNNs  

作　　者：Majdi Rawashdeh Muath A.Obaidat Meryem Abouali Dhia Eddine Salhi Kutub Thakur 

机构地区：[1]Department of Business Information Technology,Princess Sumaya University for Technology,Amman,11941,Jordan [2]Department of Computer Engineering,Al Yamamah University,Riyadh,11512,Saudi Arabia [3]Department of Computer Science,John Jay College and the Graduate Center,The City University of New York,New York,NY 10019,USA [4]Department of Computer Science,John Jay College at the City University of NewYork,NewYork,NY 10019,USA [5]LIMOSE Laboratory,Department of Computer Science,M’hamed Bougara University,Boumerdes,35000,Algeria [6]Department of Professional Security Studies,New Jersey City University,Jersey City,NJ 07305,USA

出　　处：《Computer Modeling in Engineering & Sciences》2025年第4期1129-1155,共27页工程与科学中的计算机建模(英文)

摘　　要：Cancer is a formidable andmultifaceted disease driven by genetic aberrations and metabolic disruptions.Around 19% of cancer-related deaths worldwide are attributable to lung and colon cancer,which is also the top cause of death worldwide.The malignancy has a terrible 5-year survival rate of 19%.Early diagnosis is critical for improving treatment outcomes and survival rates.The study aims to create a computer-aided diagnosis(CAD)that accurately diagnoses lung disease by classifying histopathological images.It uses a publicly accessible dataset that includes 15,000 images of benign,malignant,and squamous cell carcinomas in the lung.In addition,this research employs multiscale processing to extract relevant image features and conducts a comprehensive comparative analysis using four Convolutional Neural Network(CNN)based on pre-trained models such as AlexNet,VGG(Visual Geometry Group)16,ResNet-50,and VGG19,after hyper-tuning these models by optimizing factors such as batch size,learning rate,and epochs.The proposed(CNN+VGG19)model achieves the highest accuracy of 99.04%.This outstanding performance demonstrates the potential of the CAD system in accurately classifying lung cancer histopathological images.This study contributes significantly to the creation of a more precise CNN-based model for lung cancer identification,giving researchers and medical professionals in this vital sector a useful tool using advanced deep learning techniques and publicly available datasets.

关 键 词：Lung cancer machine learning computer aided diagnosis CNN medical imaging transfer learning 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]