Download PDFOpen PDF in browserOptimal Optimizer for Kidney Cut Prediction15 pages•Published: August 6, 2024AbstractKidney abnormality is one of the major concerns in modern society, affecting millions of people worldwide. To diagnose different kidney abnormalities, a narrow-beam X-ray imaging procedure called computed tomography is commonly used, creating cross- sectional slices of the kidney. Deep learning models have shown promise in classifying and segmenting kidney abnormalities from these CT images. However, these models often present challenges for clinicians in interpreting their decisions, leading to a "black box" system. In response to this issue, this study proposes a transfer learning technique for the detection of kidney cysts, stones, and tumors. With improved results and enhanced interpretive power, the proposed work empowers clinicians with conclusive and understandable outcomes. Furthermore, this research delves into the selection of the optimal optimizer for kidney abnormality prediction. The study performs a comprehensive performance evaluation of three popular optimizers - Adam, Adadelta, and AdamW - on coronal and axial abdominal and program images. The findings shed light on the most suitable optimizer, ensuring better training and generalization of the transfer learning model. Finally Leveraging the Flask framework, the developed web application enables seamless and efficient prediction of kidney abnormalities, potentially revolutionizing the clinical decision-making process and enhancing patient care.Keyphrases: image analyzer, kidney abnormality, medical infulencer In: Rajakumar G (editor). Proceedings of 6th International Conference on Smart Systems and Inventive Technology, vol 19, pages 181-195.
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