Retrieval of Brain Image Using Soft Computing Technique

Esther, J.; Sathik, M. Mohamed

doi:10.1109/icica.2014.104

Cited by 6 publications

(1 citation statement)

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“…It can demonstrate that a simple machine learning classifier gives in higher classification accuracy. Esther et al, [16]…”

Section: Related Workmentioning

confidence: 99%

Analysis on Shape Image Retrieval Using DNN and ELM Classifiers for MRI Brain Tumor Images

Pandian¹,

Balasubramanian²

2016

IJIEEB

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The problem of searching a digital image in a very huge database is called Content Based Image Retrieval (CBIR). Shape is a significant cue for describing objects. In this paper, we have developed a shape feature extraction of MRI brain tumor image retrieval. We used T1 weighted image of MRI brain tumor images. There are two modules: feature extraction process and classification. First, the shape features are extracted using techniques like Scale invariant feature transform (SIFT), Harris corner detection and Zernike Moments. Second, the supervised learning algorithms like Deep neural network (DNN) and Extreme learning machine (ELM) are used to classify the brain tumor images. Experiments are performed using 1000 brain tumor images. In the performance evaluation, sensitivity, specificity, accuracy, error rate and f-measure are five measures are used. The Experiment result shows that highest average accuracy has got at Zernike Moments-99%. So, Zernike Moments are better than SIFT and Harris corner detection techniques. The average time taken for DNN-0.0901 sec, ELM-0.0218 sec. So, ELM classifier is better than DNN. It increases the retrieval time and improves the retrieval accuracy significantly.

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“…It can demonstrate that a simple machine learning classifier gives in higher classification accuracy. Esther et al, [16]…”

Section: Related Workmentioning

confidence: 99%