An Enhanced Lung Cancer Detection Approach Using Dual-Model Deep Learning Technique  

作　　者：Sumaia Mohamed Elhassan Saad Mohamed Darwish Saleh Mesbah Elkaffas 

机构地区：[1]Department of Information Technology,Institute of Graduate Studies and Research,Alexandria University,Alexandria,21526,Egypt [2]College of Computing and Information Technology,Arab Academy for Science,Technology and Maritime Transport,Alexandria,P.O.Box 1029,Egypt

出　　处：《Computer Modeling in Engineering & Sciences》2025年第1期835-867,共33页工程与科学中的计算机建模（英文）

摘　　要：Lung cancer continues to be a leading cause of cancer-related deaths worldwide,emphasizing the critical need for improved diagnostic techniques.Early detection of lung tumors significantly increases the chances of successful treatment and survival.However,current diagnostic methods often fail to detect tumors at an early stage or to accurately pinpoint their location within the lung tissue.Single-model deep learning technologies for lung cancer detection,while beneficial,cannot capture the full range of features present in medical imaging data,leading to incomplete or inaccurate detection.Furthermore,it may not be robust enough to handle the wide variability in medical images due to different imaging conditions,patient anatomy,and tumor characteristics.To overcome these disadvantages,dual-model or multi-model approaches can be employed.This research focuses on enhancing the detection of lung cancer by utilizing a combination of two learning models:a Convolutional Neural Network(CNN)for categorization and the You Only Look Once(YOLOv8)architecture for real-time identification and pinpointing of tumors.CNNs automatically learn to extract hierarchical features from raw image data,capturing patterns such as edges,textures,and complex structures that are crucial for identifying lung cancer.YOLOv8 incorporates multiscale feature extraction,enabling the detection of tumors of varying sizes and scales within a single image.This is particularly beneficial for identifying small or irregularly shaped tumors that may be challenging to detect.Furthermore,through the utilization of cutting-edge data augmentation methods,such as Deep Convolutional Generative Adversarial Networks(DCGAN),the suggested approach can handle the issue of limited data and boost the models’ability to learn from diverse and comprehensive datasets.The combined method not only improved accuracy and localization but also ensured efficient real-time processing,which is crucial for practical clinical applications.The CNN achieved an accuracy of 97.67%in cl

关 键 词：Lung cancer detection dual-model deep learning technique data augmentation CNN YOLOv8 

分 类 号：R734.2[医药卫生—肿瘤] TP3[医药卫生—临床医学]