Ensemble-ASNet: An Optimized Weighted CNN Ensemble for Multi-Class Kidney Lesion Classification Using CT Images

Ensemble-ASNet: An Optimized Weighted CNN Ensemble for Multi-Class Kidney Lesion Classification Using CT Images | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 5 January 2026 V1 Latest version Share on Ensemble-ASNet: An Optimized Weighted CNN Ensemble for Multi-Class Kidney Lesion Classification Using CT Images Author : Sabib Ahmed Authors Info & Affiliations https://doi.org/10.22541/au.176761828.83809645/v1 155 views 60 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Preventing the progression of chronic kidney disease and minimizing diagnostic delays requires accurate detection of renal abnormalities such as cysts, nephrolithiasis, tumors, and normal tissue. Although deep learning-based medical imaging has made significant progress, many current models still suffer from issues such as overfitting, uneven class performance, poor interpretability, and difficulty combining extra feature representations effectively. To overcome these difficulties, this work proposes Ensemble-ASNet. This optimized weighted soft-voting ensemble combines three highly effective pre-trained CNN backbones: InceptionV3, VGG16, and MobileNetV2, to enhance robustness and discriminative power in four-class renal CT classification. Optimal model weights were determined through a constrained optimization strategy applied to validation predictions. Ensemble-ASNet outperformed all individual models and numerous cutting-edge CNN, transformer, and hybrid ensemble techniques documented in the literature, achieving 99.42% accuracy, precision, recall, and F1-score. Grad-CAM++ visualizations demonstrate that the model concentrates on clinically significant anatomical regions, which improves interpretability and decision-making. Future research will examine multi-institutional datasets and lightweight ensemble deployment; limitations include the absence of Hounsfield Unit-calibrated imaging and patient-level validation. Overall, the results demonstrate that Ensemble-ASNet has significant potential for integration into clinical diagnostic workflows and offers a dependable and efficient framework for automated renal CT analysis. Supplementary Material File (manuscript - wiley.docx) Download 3.66 MB Information & Authors Information Version history V1 Version 1 05 January 2026 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords deep learning ensemble grad-cam++ explainability kidney lesion detection optimized weighted soft voting renal ct classification Authors Affiliations Sabib Ahmed View all articles by this author Metrics & Citations Metrics Article Usage 155 views 60 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Sabib Ahmed. Ensemble-ASNet: An Optimized Weighted CNN Ensemble for Multi-Class Kidney Lesion Classification Using CT Images. Authorea . 05 January 2026. DOI: https://doi.org/10.22541/au.176761828.83809645/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . Format Please select one from the list RIS (ProCite, Reference Manager) EndNote BibTex Medlars RefWorks Direct import Tips for downloading citations document.getElementById('citMgrHelpLink').addEventListener('click', function() { popupHelp(this.href); return false; }); $(".js__slcInclude").on("change", function(e){ if ($(this).val() == 'refworks') $('#direct').prop("checked", false); $('#direct').prop("disabled", ($(this).val() == 'refworks')); }); View Options View options PDF View PDF Figures Tables Media Share Share Share article link Copy Link Copied! Copying failed. 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