Brain Tumour Image Classification using CNN and VGG |
Author(s): |
| Shruti Agrawal , Acropolis Institute of Technology and Research; Vaishnavi Rathore, Acropolis Institute of Technology and Research; Sharnam Singhai, Acropolis Institute of Technology and Research |
Keywords: |
| Neural Networks, MRI, Brain Image |
Abstract |
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The brain tumors are the most common, and aggressively increasing abnormal cells, leading to a very short life expectancy in the higher grades. Thus, to improve the quality of life of patients, treatment planning is necessary. In this field, generally Magnetic Resonance Imaging (commonly known as MRI) is used to evaluate tumors in the human brain. To reduce the difficulties in manual classification of tumor vs non-tumor scans due to the huge amount of data and the threat to accurate quantitative measurements resulting in increased death rate, a trusted and automatic classification scheme is essential. In this work, a comparison is proposed between the two types of CNNs used in image classification. The automatic brain tumor detection is performed using a custom-built Convolutional Neural Network and a pre-trained VGG-16 model. |
Other Details |
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Paper ID: IJSRDV8I20841 Published in: Volume : 8, Issue : 2 Publication Date: 01/05/2020 Page(s): 405-408 |
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