Optimizing Deep Learning Parameters for Enhanced Image Classification Accuracy: A Theoretical and Empirical Analysis

Abstract

This study investigates the,impact of key deep learning,parameters—algorithm,type, dataset size, learning,rate, batch size, and number,of epochs—on image,classification accuracy, a,critical concern,in industries such as,healthcare, security, and,technology. Despite,advancements in deep learning, optimizing,these parameters for maximum,accuracy remains,challenging. To address,this issue, a,survey was conducted,among 381 professionals and,academics experienced,in deep learning, and,the data was analyzed,using Partial Least Squares,Structural Equation,Modeling (PLS-SEM). The,results confirm that all,five parameters significantly,influence image classification,accuracy, with optimized,settings leading to substantial,improvements. This,study contributes to,the theoretical understanding,of the Bias-Variance,Tradeoff Theory in the,context of deep learning,and offers practical guidelines,for enhancing model,performance. However, limitations,such as reliance on,self-reported data suggest,the need for further,experimental research. The,findings provide a comprehensive,framework for optimizing,deep learning models, offering,both academic insights and,practical solutions for,improving accuracy in,critical applications.