International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | Automated Kidney Stone Analysis Through Deep Learining Based Segmentation Models |
|---|---|
| ABSTRACT | Kidney abnormalities, including stones, cysts, and tumors, can be effectively detected using ultrasound imaging, though low contrast and speckle noise pose challenges. This study applies preprocessing techniques—image restoration, Gabor filtering, and histogram equalization—to enhance image quality and visibility of internal structures. Accurate kidney segmentation is achieved using a level set method with momentum and resilient propagation (Rprop) terms. Extracted kidney regions are analyzed with wavelet transforms, including Symlets, Biorthogonal, and Daubechies subbands, to compute energy features indicative of stone presence. These features are then used to train Multilayer Perceptron (MLP) and Convolutional Neural Network (CNN) classifiers. The proposed approach enables precise detection and identification of kidney stones. Experimental results demonstrate high accuracy and reliability in stone classification. |
| AUTHOR | PRIYADHARSHINI M, S.ARULARASI, K.VIJAYPRABAKARAN, DHIVYABHARATHI R, VINOPRIYA K Dept. of Computer Science and Engineering, Gnyanamani College of Technology, Namakkal, India Assistant Professor, Dept. of Computer Science and Engineering, Gnyanamani College of Technology, Namakkal, India |
| VOLUME | 185 |
| DOI | DOI: 10.15680/IJIRCCE.2026.1406098 |
| pdf/98_Automated Kidney Stone Analysis Through Deep Learining Based Segmentation Models.pdf | |
| KEYWORDS | |
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