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@article{159303,
        author = {Mohammed Multazim Ansari  and Rushikesh Durgade  and Alisha Fatima Ansari  and Jaya Jeswani},
        title = {Chronic Kidney Disease Detection Using Deep Learning },
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {9},
        number = {12},
        pages = {244-248},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=159303},
        abstract = {Chronic kidney disease (CKD) is a growing health problem worldwide. Early detection of CKD is crucial for effective management of the disease. In recent years, convolutional neural networks (CNNs) have shown great potential for image recognition and classification tasks. In this study, we propose a CNN-based method for the detection of CKD from kidney ultrasound images. The methodology involves preprocessing of the ultrasound images to remove noise and artifacts. The preprocessed images are then fed into a CNN model consisting of multiple convolutional layers, pooling layers, and fully connected layers. The performance of the model is evaluated using various metrics such as accuracy, precision, recall, and F1 score. The results show that the proposed CNN-based method achieves high accuracy and can effectively classify ultrasound images as CKD or nonCKD. This method has the potential to be a useful tool for early detection and management of CKD, which can ultimately improve patient outcomes.},
        keywords = {implementation, CKD, CNN, Image Processing
},
        month = {},
        }