Beyond Algorithms: The Impact of Simplified CNN Models and Multifactorial Influences on Radiological Image Analysis

Abstract

Abstract This paper demonstrates that simplified Convolutional Neural Network (CNN) models can outperform traditional complex architectures, such as VGG-16, in the analysis of radiological images, particularly in datasets with fewer samples. We introduce two adopted CNN architectures, LightCnnRad and DepthNet, designed to optimize computational efficiency while maintaining high performance. These models were applied to nine radiological image datasets, both public and in-house, including MRI, CT, X-ray, and Ultrasound, to evaluate their robustness and generalizability. Our results show that these models achieve competitive accuracy with lower computational costs and resource requirements. This finding underscores the potential of streamlined models in clinical settings, offering an effective and efficient alternative for radiological image analysis. The implications for medical diagnostics are significant, suggesting that simpler, more efficient algorithms can deliver better performance, challenging the prevailing reliance on transfer learning and complex models. The complete codebase and detailed architecture of the LightCnnRad and DepthNet, along with step-by-step instructions, are accessible in our GitHub repository at https://github.com/PKhosravi-CityTech/LightCNNRad-DepthNet.