A Hybrid CNN KNN Model for MRI brain Tumor Classification
B. Srinivas1, G. Sasibhushana Rao2 

1B. Srinivas, MVGR College of Engg (A), Vizianagaram, India.
2G. Sasibhushana Rao, Dept. of ECE, AU College of Engg (A), Visakhapatanam, India.

Manuscript received on 03 March 2019 | Revised Manuscript received on 07 March 2019 | Manuscript published on 30 July 2019 | PP: 5230-5235 | Volume-8 Issue-2, July 2019 | Retrieval Number: B1051078219/19©BEIESP | DOI: 10.35940/ijrte.B1051.078219
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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: This paper proposes a hybrid model (CNN-KNN) for Magneto Resonance Image (MRI) brain tumor classification, which integrates convolutional neural networks (CNNs) with K-Nearest Neighbor (KNN). The CNN model is considered to extract the features and then applied to KNN classifier to predict the classes. Experiments are conducted on an open dataset images chosen from BraTS 2015 and BraTS 2017 database for classification. An accuracy of 96.25% is the performance shown using this proposed method on the test set and proven to be better in terms of accuracy, error rate, F-1 score, sensitivity, and specificity based on experimental results.
Keywords: Convolutional Neural Networks, K-Nearest Neighbor, Image Classification, Hybrid CNN-KNN, MRI Brain Tumor Classification.

Scope of the Article: Classification