S Sudharson1, Priyanka Kokil2. 1. Department of Electronics and Communication Engineering, Indian Institute of Information Technology, Design and Manufacturing, Kancheepuram, Chennai 600127, India. 2. Department of Electronics and Communication Engineering, Indian Institute of Information Technology, Design and Manufacturing, Kancheepuram, Chennai 600127, India. Electronic address: priyanka@iiitdm.ac.in.
Abstract
BACKGROUND AND OBJECTIVE: Chronic kidney disease is a worldwide health issue which includes not only kidney failure but also complications of reduced kidney functionality. Cyst formation, nephrolithiasis or kidney stone, and renal cell carcinoma or kidney tumor are the common kidney disorders which affects the functionality of kidneys. These disorders are typically asymptomatic, therefore early and automatic diagnosis of kidney disorders are required to avoid serious complications. METHODS: This paper proposes an automatic classification of B-mode kidney ultrasound images based on the ensemble of deep neural networks (DNNs) using transfer learning. The ultrasound images are usually affected by speckle noise and quality selection in the ultrasound image is based on perception-based image quality evaluator score. Three variant datasets are given to the pre-trained DNN models for feature extraction followed by support vector machine for classification. The ensembling of different pre-trained DNNs like ResNet-101, ShuffleNet, and MobileNet-v2 are combined and final predictions are done by using the majority voting technique. By combining the predictions from multiple DNNs the ensemble model shows better classification performance than the individual models. The presented method proved its superiority when compared to the conventional and DNN based classification methods. The developed ensemble model classifies the kidney ultrasound images into four classes, namely, normal, cyst, stone, and tumor. RESULTS: To highlight effectiveness of the proposed approach, the ensemble based approach is compared with the existing state-of-the-art methods and tested in the variants of ultrasound images like in quality and noisy conditions. The presented method resulted in maximum classification accuracy of 96.54% in testing with quality images and 95.58% in testing with noisy images. The performance of the presented approach is evaluated based on accuracy, sensitivity, and selectivity. CONCLUSIONS: From the experimental analysis, it is clear that the ensemble of DNNs classifies the majority of images correctly and results in maximum classification accuracy as compared to the existing methods. This automatic classification approach is a supporting tool for the radiologists and nephrologists for precise diagnosis of kidney diseases.
BACKGROUND AND OBJECTIVE:Chronic kidney disease is a worldwide health issue which includes not only kidney failure but also complications of reduced kidney functionality. Cyst formation, nephrolithiasis or kidney stone, and renal cell carcinoma or kidney tumor are the common kidney disorders which affects the functionality of kidneys. These disorders are typically asymptomatic, therefore early and automatic diagnosis of kidney disorders are required to avoid serious complications. METHODS: This paper proposes an automatic classification of B-mode kidney ultrasound images based on the ensemble of deep neural networks (DNNs) using transfer learning. The ultrasound images are usually affected by speckle noise and quality selection in the ultrasound image is based on perception-based image quality evaluator score. Three variant datasets are given to the pre-trained DNN models for feature extraction followed by support vector machine for classification. The ensembling of different pre-trained DNNs like ResNet-101, ShuffleNet, and MobileNet-v2 are combined and final predictions are done by using the majority voting technique. By combining the predictions from multiple DNNs the ensemble model shows better classification performance than the individual models. The presented method proved its superiority when compared to the conventional and DNN based classification methods. The developed ensemble model classifies the kidney ultrasound images into four classes, namely, normal, cyst, stone, and tumor. RESULTS: To highlight effectiveness of the proposed approach, the ensemble based approach is compared with the existing state-of-the-art methods and tested in the variants of ultrasound images like in quality and noisy conditions. The presented method resulted in maximum classification accuracy of 96.54% in testing with quality images and 95.58% in testing with noisy images. The performance of the presented approach is evaluated based on accuracy, sensitivity, and selectivity. CONCLUSIONS: From the experimental analysis, it is clear that the ensemble of DNNs classifies the majority of images correctly and results in maximum classification accuracy as compared to the existing methods. This automatic classification approach is a supporting tool for the radiologists and nephrologists for precise diagnosis of kidney diseases.