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KidNet: Kidney Tumour Diagnosis System Design Using Deep Convolutional Neural Network

KidNet: Kidney Tumour Diagnosis System Design Using Deep Convolutional Neural Network
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Author(s): Umamaheswari S. (Anna University, MIT Campus, India), Sangeetha D. (Anna University, MIT Campus, India), C. Mouliganth (Anna University, India) and Vignesh E. M. (Anna University, India)
Copyright: 2021
Pages: 16
Source title: Deep Learning Applications and Intelligent Decision Making in Engineering
Source Author(s)/Editor(s): Karthikrajan Senthilnathan (Revoltaxe India Pvt Ltd, Chennai, India), Balamurugan Shanmugam (Quants IS & CS, India), Dinesh Goyal (Poornima Institute of Engineering and Technology, India), Iyswarya Annapoorani (VIT University, India) and Ravi Samikannu (Botswana International University of Science and Technology, Botswana)
DOI: 10.4018/978-1-7998-2108-3.ch004


View KidNet: Kidney Tumour Diagnosis System Design Using Deep Convolutional Neural Network on the publisher's website for pricing and purchasing information.


Kidney cancer is one of the 10 most common cancers in both men and women. The lifetime risk for one developing kidney cancer is about 1.6%. The rate of kidney cancer diagnosis has been rising since the 1990s due to the use of newer imaging tests such as CT scans. The kidneys are deep inside the body and hence small kidney tumours cannot be seen or felt during a physical examination. Existing work on kidney tumour diagnosis uses traditional machine learning and image processing techniques to find and classify the images. Deep learning systems do not require this domain-specific knowledge. The kidney tumour diagnosis system uses deep learning and convolutional neural networks to classify CT images. A deep learning neural network model named KidNet has been implemented. It has been trained using labelled kidney CT images. To achieve acceleration during the training phase, GPUs have been used. The network when trained with abdominal CT images achieved 86.1% accuracy, and the one trained with cropped portion of kidney images achieved 89.6% accuracy.

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