{"paper_id":"35da834e-7701-4bf3-8abe-b605fd94cba5","body_text":"Abstract\nThis 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.\nCompeting Interest Statement\nThe authors have declared no competing interest.\nClinical Protocols\nhttps://github.com/PKhosravi-CityTech/LightCNNRad-DepthNet\nFunding Statement\nThis study did not receive any funding\nAuthor Declarations\nI confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.\nYes\nThe details of the IRB/oversight body that provided approval or exemption for the research described are given below:\nEthics committee/IRB of AdventHealth in Orlando, FL, gave ethical approval for this work under protocol number 3009855250.\nI confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.\nYes\nI understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).\nYes\nI have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.\nYes","source_license":"CC-BY-4.0","license_restricted":false}