International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 12 Issue: 01 | Jan 2025
p-ISSN: 2395-0072
www.irjet.net
KidneyNet-V: Automated Kidney Stone Detection in CT Scans Using a Customized MobileNetV2 Model Komare Vishnu Vardhan1, Ponnala Vihaarika Reddy2, Dr. S. Sreekanth3 1B. Tech Student, Dept. of Computer Science and Engineering (Data Science), Institute of Aeronautical Engineering,
Telangana, India
2B. Tech Student, Dept. of Computer Science and Engineering (Data Science), Institute of Aeronautical Engineering,
Telangana, India Associate Professor & Deputy Head, Dept. of Computer Science and Engineering (Data Science), Institute of Aeronautical Engineering, Telangana, India ---------------------------------------------------------------------***--------------------------------------------------------------------3
interpretation of CT scans is slow, error-prone, and operator-dependent on radiologists' experience. To address these limitations, artificial intelligence (AI) has emerged as a promising solution in the field of medical imaging. Deep learning models, in particular, have demonstrated remarkable success in automating the analysis of medical images, improving both diagnostic speed and accuracy. Yet, the widespread adoption of AI-based diagnostic systems is often hindered by the complexity and computational demands of many existing models. These models typically require large datasets, extensive computational resources, and considerable processing time, making them difficult to implement in real-time diagnostic settings or resourceconstrained environments.
Abstract - Kidney stones represent a significant health
concern, leading to numerous hospitalizations and severe pain. Timely diagnosis is vital for proper treatment and to avoid complications like kidney failure. This paper presents KidneyNet-V, a streamlined and effective deep learning model based on a customized MobileNetV2 architecture, aimed at automatically detecting kidney stones from CT scans. This model was developed using a dataset which consists of 1799 CT images, divided into two categories: those with and without kidney stones. Our approach incorporates advanced techniques such as global average pooling, dropout layers, and data augmentation, which enhance This model's robustness and efficiency. The evaluation metrics reveal that KidneyNet-V achieves an accuracy of 99.71%, with exceptional precision, recall, and F1 scores, demonstrating its effectiveness in diagnosing kidney stones. In addition, comparisons with other deep learning models highlight KidneyNet-V's enhanced accuracy and efficiency, as well as its lower computational demands. A user-friendly web application, built with Streamlit, enables healthcare professionals to upload CT images and receive instantaneous predictions, thereby enhancing the speed of diagnosis. This work illustrates the potential of KidneyNet-V as a valuable tool in medical decision support systems, paving the way for future innovations in automated healthcare solutions. Key Words: Kidney stones, Deep learning, KidneyNet-V, MobileNetV2, CT scans, Data augmentation, Medical decision support, Automated diagnosis, Healthcare solutions, Efficient, Automated, Streamlit
1.INTRODUCTION Kidney stones are a prevalent urological condition that affect millions of individuals worldwide, often resulting in severe pain, hospitalizations, and long-term complications like chronic kidney disease or renal failure. Early detection and diagnosis are vital to preventing these outcomes and ensuring timely treatment. Traditionally, CT scans are considered the gold standard in the detection of kidney stones because of their high sensitivity and ability to visualize the size, shape, and location of stones. Manual
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Fig -1: Flowchart illustrating the project workflow for kidney stone detection. This paper introduces KidneyNet-V, an optimized deep learning model tailored for the automated kidney stone identification from CT images. This model is both efficient and lightweight, ensuring effective performance without
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