Efficient Approach for Kidney Stone Treatment Using Convolutional Neural Network

Abstract

Kidney stone treatment is a critical task because untreated kidney stones can lead to severe pain, kidney damage, and potentially life-threatening complications such as infections and blockages of the urinary tract.The ToC (Time of Conversion) and Accuracy of Diagnosis are very low with earlier models.According to World Health Organization (WHO), every 1 in 11 people are affected by kidney stones.Current diagnostic methods face challenges in identifying the affected area and location of cysts and tumors.Elastic Net Regression (ENR), Logistic Regression (LR) and Machine Learning models are less accurate in finding the anomalies.Therefore, for the sake of future generations, it is essential to create a sophisticated kidney abnormality detection application.This research successfully presents a Convolutional Neural Network (CNN) based approach for the classification of Computed Tomography (CT) kidney images into four categories: Normal, Cyst, Tumor, and Stone.The dataset, curated from different hospitals in Dhaka, Bangladesh, contains 12,446 images, with a balanced representation of Normal, Cyst, Tumor, and Stone categories.In terms of CNN architecture, our model comprises multiple convolutional layers, max-pooling layers, and fully connected layers.The convolutional layers apply learnable filters to detect patterns and features, followed by Rectified Linear Unit (ReLU) activation functions to introduce nonlinearity.Max-pooling layers downsample feature maps, enhancing computational efficiency.Fully connected layers facilitate classification by learning complex patterns.The proposed methodology leverages the power of deep learning to automate the recognition of kidney conditions, aiding radiologists in their diagnostic tasks.The methodology involves preprocessing of CT images, followed by feature extraction and classification using the CNN model.The research evaluates the approach on a curated CT kidney dataset, achieving promising results, and discusses the potential for future improvements and applications in clinical practice.In comparison to existing literature, the proposed work demonstrates significant advancements in kidney abnormality detection.The model's performance measures, including Accuracy (99.57%),F1-score (99.34%),Recall (99.56%) and Precision (99.58%), far surpass those of previous methodologies.The proposed application outperforms the methodology and competes with present models.

Topics & Concepts