Optimizing Deep Learning for Computer-Aided Diagnosis of Lung Diseases: An Automated Method Combining Evolutionary Algorithm, Transfer Learning, and Model Compression

作     者：Hassen Louati Ali Louati Elham Kariri Slim Bechikh 

作者机构：College of Information TechnologyKingdom UniversityRiffa40434Bahrain SMART LabUniversity of TunisISGTunisTunisia Department of Information SystemsCollege of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia 

出 版 物：《Computer Modeling in Engineering & Sciences》 (工程与科学中的计算机建模（英文）)

年 卷 期：2024年第138卷第3期

页      面：2519-2547页

核心收录：

学科分类：1002[医学-临床医学] 08[工学] 080203[工学-机械设计及理论] 100201[医学-内科学(含：心血管病、血液病、呼吸系病、消化系病、内分泌与代谢病、肾病、风湿病、传染病)] 0802[工学-机械工程] 10[医学] 

基　　金：via funding from Prince Sattam bin Abdulaziz University Project Number(PSAU/2023/R/1444) 

主　　题：Computer-aided diagnosis deep learning evolutionary algorithms deep compression transfer learning 

摘      要：Recent developments in Computer Vision have presented novel opportunities to tackle complex healthcare issues,particularly in the field of lung disease *** promising avenue involves the use of chest X-Rays,which are commonly utilized in *** fully exploit their potential,researchers have suggested utilizing deep learning methods to construct computer-aided diagnostic ***,constructing and compressing these systems presents a significant challenge,as it relies heavily on the expertise of data *** tackle this issue,we propose an automated approach that utilizes an evolutionary algorithm(EA)to optimize the design and compression of a convolutional neural network(CNN)for X-Ray image *** approach accurately classifies radiography images and detects potential chest abnormalities and infections,including ***,our approach incorporates transfer learning,where a pre-trainedCNNmodel on a vast dataset of chest X-Ray images is fine-tuned for the specific task of detecting *** method can help reduce the amount of labeled data required for the task and enhance the overall performance of the *** have validated our method via a series of experiments against state-of-the-art architectures.