Open AccessClassification of Content based Medical Image Retrieval Using Texture and Shape feature with Neural Network
Author(s) -
Sweety Maniar,
Jainam Shah
Publication year - 2017
Publication title -
international journal of advances in applied sciences
Language(s) - English
Resource type - Journals
eISSN - 2722-2594
pISSN - 2252-8814
DOI - 10.11591/ijaas.v6.i4.pp368-374
Subject(s) - artificial intelligence , pattern recognition (psychology) , naive bayes classifier , decision tree , computer science , artificial neural network , contextual image classification , feature (linguistics) , image (mathematics) , image retrieval , machine learning , support vector machine , linguistics , philosophy
Medical image classification and retrieval systems have been finding extensive use in the areas of image classification according to imaging modalities, body part and diseases. One of the major challenges in the medical classification is the large size images leading to a large number of extracted features which is a burden for the classification algorithm and the resources. In this paper, a novel approach for automatic classification of fundus images is proposed. The method uses image and data pre-processing techniques to improve the performance of machine learning classifiers. Some predominant image mining algorithms such as Classification, Regression Tree (CART), Neural Network, Naive Bayes (NB), Decision Tree (DT) K-Nearest Neighbor. The performance of MCBIR systems using texture and shape features efficient. . The possible outcomes of a two class prediction be represented as True positive (TP), True negative (TN), False Positive (FP) and False Negative (FN).