Automated Detection of Kidney Stones and Their Characteristics in Kidney Ultrasound Images: Size, Area, and Location
Keywords:
Ultrasound Images, Speckle Noise, Stone Localization, Stone Size Measurement, Laplacian Function, CNNAbstract
Ultrasound imaging is a widely adopted modality for kidney stone detection owing to its non-invasive nature and cost-effectiveness. However, challenges such as speckle noise and low contrast hinder the accuracy of diagnoses. This research addresses these challenges by focusing on enhancing ultrasound image quality, specifically targeting the precise localization and measurement of kidney stones. The proposed system employs Median filtering and contrast enhancement techniques to mitigate speckle noise and improve image quality, presenting a thorough comparative analysis with alternative filters. Additionally, the study explores the utilization of the Laplacian function for stone localization and size measurement. Moreover, this research introduces a Computer-Aided System for Kidney Stone Detection in Ultrasound Images, leveraging a Convolutional Neural Network (CNN). The balanced dataset, consisting of 9416 images categorized as 'Normal' and 'Stone,' facilitates robust training and testing of the CNN. Trained with Stochastic Gradient Descent (SGD), the CNN exhibits excellent performance with a training accuracy of 99.10% and a test accuracy of 99.11%.
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