{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,23]],"date-time":"2026-07-23T08:00:44Z","timestamp":1784793644140,"version":"3.55.0"},"reference-count":33,"publisher":"SAGE Publications","issue":"5","license":[{"start":{"date-parts":[[2026,1,29]],"date-time":"2026-01-29T00:00:00Z","timestamp":1769644800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Ultrason Imaging"],"published-print":{"date-parts":[[2026,9]]},"abstract":"<jats:p>\n                    Kidney stone disease is a prevalent urological disorder that can result in severe pain, obstruction, and long-term complications if not detected and managed promptly. Traditional diagnostic approaches, particularly those relying on manual assessment of ultrasound images, often suffer from limitations such as subjective interpretation, dependency on radiologist expertise, and challenges in identifying small or complex stones. These constraints can lead to diagnostic delays and inconsistencies, especially in time-sensitive or resource-limited clinical settings. Therefore, the need for an intelligent, automated solution that enhances diagnostic accuracy and efficiency is more critical than ever. To address these issues, we propose a novel deep learning-based model called the Kronecker Self-Organizing Map Forward Harmonic Network (KSOMFHNet) for kidney stone classification using ultrasound imagery. The model begins with an image preprocessing phase, where a double bilateral filter is applied to effectively denoise the ultrasound images. Following this, the Deep Recursive Residual Network (DRRN) is employed to segment the kidney region accurately. Feature extraction is then performed using a combination of Binary Robust Independent Elementary Features (BRIEF), shape-based features, and Gray Level Co-Occurrence Matrix (GLCM) texture descriptors. These features are then used for classification via the KSOMFHNet, a hybrid architecture integrating the Deep Kronecker Neural Network (DKN) and Self-Organizing Map Network (SOMNet). This fusion enhances the model\u2019s learning capacity and spatial representation abilities. Experimental results demonstrate that KSOMFHNet achieves high performance, with an accuracy of 91.984%, a True Positive Rate (TPR)\u2009of 90.543%, a True Negative Rate (TNR)\u2009of 92.248%, a precision of 90.179%, and an\n                    <jats:italic toggle=\"yes\">F<\/jats:italic>\n                    1-score of 90.360% for training data is 90%, highlighting its potential for clinical deployment.\n                  <\/jats:p>","DOI":"10.1177\/01617346251413799","type":"journal-article","created":{"date-parts":[[2026,1,30]],"date-time":"2026-01-30T04:53:48Z","timestamp":1769748828000},"page":"336-357","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":2,"title":["Ultrasound-Based Kidney Stone Classification Using Kronecker Self-Organizing Map Forward Harmonic Network"],"prefix":"10.1177","volume":"48","author":[{"given":"Pendela","family":"Kanchanamala","sequence":"first","affiliation":[{"name":"Department of CSE-AI &amp; DS, GMR Institute of Technology, Rajam, Andhra Pradesh, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2963-4272","authenticated-orcid":false,"given":"Kishore","family":"Bhamidipati","sequence":"additional","affiliation":[{"name":"Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rohini","family":"Arunachalam","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Anil Neerukonda Institute of Technology and Sciences, Visakhapatnam, Andhra Pradesh, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Boyidapu","family":"Ravi Kumar","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Anil Neerukonda Institute of Technology and Sciences, Visakhapatnam, Andhra Pradesh, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Veerraju","family":"Gampala","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Guntur, Andhra Pradesh, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Suneetha","family":"Merugula","sequence":"additional","affiliation":[{"name":"Department of CSE, GITAM School of Computer Science &amp; Engineering, GITAM (Deemed to be University), Visakhapatnam, Andhra Pradesh, India"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2026,1,29]]},"reference":[{"key":"e_1_3_2_2_2","first-page":"1","author":"Irudayaraj AA.","year":"2022","unstructured":"Irudayaraj AA. Kidney stone detection using deep learning methodologies. MSc Project, National College of Ireland; 2022, pp. 1-23.","journal-title":"Kidney stone detection using deep learning methodologies"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jksuci.2024.102130"},{"issue":"1","key":"e_1_3_2_4_2","article-title":"Kidney stone disease: an update on current concepts","volume":"2018","author":"Alelign T","year":"2018","unstructured":"Alelign T, Petros B. Kidney stone disease: an update on current concepts. Adv Urol. 2018;2018(1):3068365.","journal-title":"Adv Urol"},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2023.119005"},{"key":"e_1_3_2_6_2","doi-asserted-by":"publisher","DOI":"10.1002\/bco2.137"},{"key":"e_1_3_2_7_2","doi-asserted-by":"crossref","unstructured":"Flores-Araiza D Lopez-Tiro F Larose C Gonzalez-Mendoza M Daul C. Improving prototypical parts abstraction for case-based reasoning explanations designed for the kidney stone type recognition. arXiv preprint arXiv:2409.12883 2024.","DOI":"10.1016\/j.artmed.2025.103266"},{"key":"e_1_3_2_8_2","unstructured":"Kaur G Singh S. Kidney ultrasound images \u201cstone\u201d and \u201cno stone \u201d 2024 https:\/\/data.mendeley.com\/datasets\/h6jc4xm4py\/1"},{"key":"e_1_3_2_9_2","doi-asserted-by":"publisher","DOI":"10.1021\/acsphotonics.3c00651"},{"issue":"1","key":"e_1_3_2_10_2","article-title":"Kidney Stone detection using image segmentation and radial transform","volume":"29","author":"Kaur G","year":"2023","unstructured":"Kaur G, Singh S. Kidney Stone detection using image segmentation and radial transform. Comput Integr Manuf Syst. 2023;29(1):338-44.","journal-title":"Comput Integr Manuf Syst"},{"key":"e_1_3_2_11_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2017.07.005"},{"key":"e_1_3_2_12_2","doi-asserted-by":"publisher","DOI":"10.3390\/bioengineering10080970"},{"key":"e_1_3_2_13_2","doi-asserted-by":"publisher","DOI":"10.32628\/CSEIT2410589"},{"key":"e_1_3_2_14_2","article-title":"Automatic kidney stone detection using deep learning method","volume":"44","author":"Ramesh PS","year":"2023","unstructured":"Ramesh PS, Patel MS, Bamane KD, Rani Y, Tiwari M. Automatic kidney stone detection using deep learning method. J Adv -zool. 2023;44:100-9.","journal-title":"J Adv -zool"},{"key":"e_1_3_2_15_2","first-page":"93","article-title":"Kidney stone detection using ultrasonographic images by support vector machine classification","volume":"20","author":"Manjunatha D","year":"2024","unstructured":"Manjunatha D, Vishwakarma V, Mishra A, Ravindran E, Kumari K. Kidney stone detection using ultrasonographic images by support vector machine classification. Nanotechnol Percept. 2024;20:93.","journal-title":"Nanotechnol Percept"},{"issue":"2","key":"e_1_3_2_16_2","article-title":"A hybrid model for kidney stone detection using deep learning","volume":"13","author":"Kumar P","year":"2024","unstructured":"Kumar P, Samagh JS, Singh D. A hybrid model for kidney stone detection using deep learning. Int J Sci Technol Manag. 2024;13(2):65-85.","journal-title":"Int J Sci Technol Manag"},{"key":"e_1_3_2_17_2","article-title":"Computer-aided diagnosis system for the classification of multi-class kidney abnormalities in the noisy ultrasound images. Comput Methods Programs","volume":"205","author":"Sudharson S","year":"2021","unstructured":"Sudharson S, Kokil P. Computer-aided diagnosis system for the classification of multi-class kidney abnormalities in the noisy ultrasound images. 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TPR, PPV and ROC based performance measurement and optimization of human face recognition of iot enabled physical location monitoring. 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