A Novel Framework for Detection and Classification of Brain Hemorrhage
Nita Kakhandaki1, S. B. Kulkarni2

1Nita Kakhandaki, Research Scholar, SDM College of Engineering and Technology, Affiliated to Visvesvaraya Technological University, Belgaum (Karnataka), India.
2Dr. S. B. Kulkarni, Associate Professor, SDM College of Engineering and Technology, Dharwad (Karnataka), India.

Manuscript received on 24 September 2018 | Revised Manuscript received on 30 September 2018 | Manuscript published on 30 November 2018 | PP: 86-93 | Volume-7 Issue-4, November 2018 | Retrieval Number: E1805017519©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: The proposed work focuses on detecting the correct location and type of the hemorrhage in MR Brain image. The Gradient Recalled Echo MR Images are considered as the input image. Then a region and structure specific Multi level Set evolution algorithm is implemented to segment the hemorrhagic region. An enhanced Local Tetra pattern based feature extraction algorithm is used to extract sharpened tetra features and the features are optimized by applying an enhanced Grey Wolf Optimization algorithm. Finally, a Relevance Vector Machine based Classifier is designed to classify the types of the hemorrhages. The proposed framework is compared with the existing techniques on the scale of accuracy, sensitivity, specificity, precision, Jaccard, Dice and kappa coefficient and proved to be outperforming.
Keywords: Brain Hemorrhage, Multi-Level Set algorithm, Local Tetra Pattern, Grey Wolf Optimizer, Relevance Vector Machine.

Scope of the Article: Artificial Intelligence and Machine Learning