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All Journal Jurnal Pendidikan Matematika dan IPA JUITA : Jurnal Informatika Journal of Natural Sciences and Mathematics Research Indonesian Journal of Chemical Analysis (IJCA) Jurnal Abdi Insani Journal Social Science And Technology For Community Service Mitra Mahajana: Jurnal Pengabdian Masyarakat Jurnal Pengabdian Nasional REACTOR : Journal of Research On Chemistry And Engineering Jurnal Teknologi Kimia Mineral Jurnal Rekayasa Proses
Afifah, Dian Ayu
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Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Physics Public Health Social Sciences

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Evaluation of CNN Architectures for Kidney Stone Classification in Ultrasound Image Zuriati, Zuriati; Sriyanto, Sriyanto; Supriyatna, Agiska Ria; Qomariyah, Nurul; Afifah, Dian Ayu; Zarnelly, Zarnelly
JUITA: Jurnal Informatika JUITA Vol. 14 Issue 1, March 2026
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

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Abstract

Kidney stone diagnosis requires fast and reliable evaluation, yet ultrasound interpretation still largely depends on clinical expertise. This study evaluates four Convolutional Neural Network (CNN) architectures, VGG16, ResNet50, MobileNetV2, and EfficientNetB0 for classifying kidney ultrasound images into Normal and Stone categories. Using a public dataset of 9,416 images, the models were assessed in terms of predictive performance and computational efficiency. MobileNetV2 achieved perfect classification performance, recording 100% accuracy, precision, recall, and F1-score, while maintaining the smallest parameter size (≈3.6M) and fastest training time (~44 s/epoch). VGG16 and ResNet50 also delivered near perfect accuracy (99.79% and 99.89%) with full recall for Stone cases. In contrast, EfficientNetB0 failed to generalize, yielding only 51.62% accuracy due to severe misclassification of Normal images. These results demonstrate that MobileNetV2 provides the most reliable and efficient solution for ultrasound based kidney stone classification, highlighting its strong potential for practical clinical deployment.
Co-Authors Aji, Salomo Pranata Alvita , Livia Rhea Alvita, Livia Rhea Analianasari Analianasari Cendekia, Devy Dewi Ermaya Dwi Setyorini Elisa Elisa Elsyana, Vida Fadian Farisan Silmi Feladita, Niken Hamdi, Rafiqul Indradewa, Rhian Nurul Qomariyah Ramandani, Adityas Agung Rezki, Amelia Sri Setiawan, Panji Shintawati shintawati, shintawati Sriyanto, Sriyanto Subiantoro, Eko Supriyatna, Agiska Ria Syafaatullah, Achmad Qodim Teguh, Dedi Variyana, Yeni Zarnelly Zarnelly Zuriati, Zuriati