Author : Mohammed Asith N 1
Date of Publication :25th October 2019
Abstract: Artificial Neural Networks are one of the most frequently used classifiers for medical diagnostic tasks to detect diseases at an earlier stage. This research work aims to diagnose ultrasound(US) kidney defects such as kidney cyst, kidney calculus, and kidney tumor. We used the Multilayer Perceptions, and obtained 98 % accuracy with 10 hidden neurons for classification between normal and abnormal. This work also seeks to provide data on the preprocessing method using median and wiener filters on how much the speckle noise was removed and how much the Fuzzy C-Means clustering was used by segmentation method. Among these methods, SIFT has been implemented for the extraction of features, and these values are given as input to the classifier. SIFT is invariant transform characteristic to obtain unique invariant characteristics. Keypoint is the location of the image where a description is computed. The local structure and the key point are summarized in the feature descriptor.
Reference :
-
- Prema T. Akkasaligar, Savithri S. Unnibhavi, “ Identification of Kidney in Medical Ultrasound Images”, in proceedings of 5th SARC-IRF International Conference, 04th May, 2014. ISN: 978-93-84209-13-1.
- Sneha A Mane, S R Chougule, “A Review on Neural Network Methodology for Diagnosis of Kidney Stone”, International Journal of Science and Research (IJSR), Volume 4 Issue 11, November 2015.
- Dr.Punal M Arabi, Surekha Nigudi, Rohith N Reddy, Dhatri, “ Classification of Kidney Stone Using GLCM ”, International Journal of Advanced Networking & Applications (IJANA), ISSN:0975-0282.
- Rajeshwari Dass, Priyanka, Swapna Devi, “ Image Segmentation techniques”, International Journal of Electronics and Communication Technology, Volume 3, Issue 1,March-2012.
- Yan Xu, Toshihiro Nishimura, “Segmentation of Breast Lesions in Ultrasound Images Using Spatial Fuzzy Clustering and Structure Tensors”, International Journal of Computer, Electrical, Automation, Control and information Engineering Vol:3, No;5, 2009.
- Mariam Wagih Attia, Hossam El-Din Moustafa, F.E.Z.Abou-Chadi, Nagham Mekky, “Classification of Ultrasound Kidney Images Using PCA and Neural Networks”, International Journal of Advanced Computer Science and Applications (IJACSA), Vol.6, No. 4, 2015.
- K.Viswanth, R.Gunasundari, “Design and Analysis Performance of kidney Stone Detection from Ultrasound Image by Level Set Segmentation and ANN Classification”, International Conference on Advances in Computing, Communications and Informatics (ICACCI), IEEE, 2014.