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A Decision Support System for Classification of Normal and Medical Renal Disease Using Ultrasound Images: A Decision Support System for Medical Renal Diseases

Komal Sharma, Jitendra Virmani

Source Title: International Journal of Ambient Computing and Intelligence (IJACI)8(2)

ISSN: 1941-6237|EISSN: 1941-6245|EISBN13: 9781522512226|DOI: 10.4018/IJACI.2017040104

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MLA

Sharma, Komal, and Jitendra Virmani. "A Decision Support System for Classification of Normal and Medical Renal Disease Using Ultrasound Images: A Decision Support System for Medical Renal Diseases." IJACI vol.8, no.2 2017: pp.52-69. http://doi.org/10.4018/IJACI.2017040104

APA

Sharma, K. & Virmani, J. (2017). A Decision Support System for Classification of Normal and Medical Renal Disease Using Ultrasound Images: A Decision Support System for Medical Renal Diseases. International Journal of Ambient Computing and Intelligence (IJACI), 8(2), 52-69. http://doi.org/10.4018/IJACI.2017040104

Chicago

Sharma, Komal, and Jitendra Virmani. "A Decision Support System for Classification of Normal and Medical Renal Disease Using Ultrasound Images: A Decision Support System for Medical Renal Diseases," International Journal of Ambient Computing and Intelligence (IJACI) 8, no.2: 52-69. http://doi.org/10.4018/IJACI.2017040104

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Abstract

Early detection of medical renal disease is important as the same may lead to chronic kidney disease which is an irreversible stage. The present work proposes an efficient decision support system for detection of medical renal disease using small feature space consisting of only second order GLCM statistical features computed from raw renal ultrasound images. The GLCM mean feature vector and GLCM range feature vector are computed for inter-pixel distance d varying from 1 to 10. These texture feature vectors are combined in various ways yielding GLCM ratio feature vector, GLCM additive feature vector and GLCM concatenated feature vector. The present work explores the potential of five texture feature vectors computed using GLCM statistics exhaustively for differential diagnosis between normal and MRD images using SVM classifier. The result of the study indicates that GLCM range feature vector computed with d = 1 yields the highest overall classification accuracy of 85.7% with individual classification accuracy values of 93.3% and 77.9% for normal and MRD classes respectively.

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A Decision Support System for Classification of Normal and Medical Renal Disease Using Ultrasound Images: A Decision Support System for Medical Renal Diseases

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