Classification of MR Brain tumors with Deep Plain and Residual Feed forward CNNs through Transfer learning
Anilkumar B1, P.Rajesh Kumar2
1Anilkumar B, Assistant Professor, Department of ECE, GMR Institute of Technology, Rajam, India.
2Dr. P. Rajesh Kumar, Professor, Department of ECE, University College of Engineering (A), Andhra University, Visakhapatnam, India.
Manuscript received on July 20, 2019. | Revised Manuscript received on August 10, 2019. | Manuscript published on August 30, 2019. | PP: 1758-1763 | Volume-8 Issue-6, August 2019. | Retrieval Number: F8437088619/2019©BEIESP | DOI: 10.35940/ijeat.F8437.088619
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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: Medical imaging plays an important role in the diagnosis of some critical diseases and further treatment process of patients. Brain is a central and most complex structure in the human body that works with billions of cells, which controls all other organ functioning. Brain tumours observed as uncontrolled abnormal cell growth in brain tissues. Classification of such cells in a early stage will increase the survival rate of the patient. Machine learning algorithms have contributed much in automation of such tasks. Further improvement in prediction rate is possible through deep learning models. In this paper presents experiments by deep transfer learning models on publicly available dataset for Brain tumour classification. Pre-trained plain and residual feed forward models such as Alexnet, VGG19, ResNet50, ResNet101 and Google Net are used for the purpose of feature extraction, Fully connected layers and softmax layer for classification is used commonly. The evaluation metrics Accuracy, Sensitivity, Specificity and F1-Score were computed.
Keywords: Brain Tumor, Classification, CNNs, Transfer Learning.