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Artificial Neural Network Based Detection of Renal Tumors using CT Scan Image Processing

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Artificial Neural Network Based Detection of Renal Tumors using CT Scan Image Processing


Gurpreet Kaur | Gargi Kalia | Preeti Sondhi



Gurpreet Kaur | Gargi Kalia | Preeti Sondhi "Artificial Neural Network Based Detection of Renal Tumors using CT Scan Image Processing" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-4, June 2019, pp.1390-1397, URL: https://www.ijtsrd.com/papers/ijtsrd25090.pdf

The segmentation, as well as analysis of renal tumor, is important to step which is performed by the doctor while deciding the stage of cancer and finding the appropriate method of its treatment. This paper determines a novel approach in order to develop an algorithm which helps in detecting and analysis of renal cancer tumors. The developed algorithm has been employed to segment and pre-processes the image for its better visualization and segment the visible tumor. The pre-processing has a hybrid filter for image enhancement and noise removal. An artificial neural network is also used by Hybrid Self Organizing Maps. It uses the clustering of image data to highlight the detected region. The appropriate output is obtained according to the medical field and it is compared with the resultant image to improve the algorithm. It helps in understanding the affected region in the human body and for better visualization. A region growing method is also applied for finding the same intensity images in images and to segment out the tumor from the processed image. The objective of this paper is to create a CT image database and then apply pre-processing methods on the image. The image segmentation is done by using Haar wavelet. The boundary is also detected by using canny. The feature extraction is applied to the image on the basis of shape, intensity, and texture and after that Fuzzy clustering is applied to get the optimized segmented image.

Renal tumors, Region growing, MRI, CT scan, ultrasound, ANN, Image Processing, Region Growing, SOM


IJTSRD25090
Volume-3 | Issue-4, June 2019
1390-1397
IJTSRD | www.ijtsrd.com | E-ISSN 2456-6470
Copyright © 2019 by author(s) and International Journal of Trend in Scientific Research and Development Journal. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0) (http://creativecommons.org/licenses/by/4.0)

International Journal of Trend in Scientific Research and Development - IJTSRD having online ISSN 2456-6470. IJTSRD is a leading Open Access, Peer-Reviewed International Journal which provides rapid publication of your research articles and aims to promote the theory and practice along with knowledge sharing between researchers, developers, engineers, students, and practitioners working in and around the world in many areas like Sciences, Technology, Innovation, Engineering, Agriculture, Management and many more and it is recommended by all Universities, review articles and short communications in all subjects. IJTSRD running an International Journal who are proving quality publication of peer reviewed and refereed international journals from diverse fields that emphasizes new research, development and their applications. IJTSRD provides an online access to exchange your research work, technical notes & surveying results among professionals throughout the world in e-journals. IJTSRD is a fastest growing and dynamic professional organization. The aim of this organization is to provide access not only to world class research resources, but through its professionals aim to bring in a significant transformation in the real of open access journals and online publishing.

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