Brain Tumour Detection and Classification using Deep Convolutional Neural Network (DCNN)

M. Naresh Babu; Akula Balakrishna; Galla Yasasri; Chalamalasetty Sri Siva; Pamarthi Vamsi

doi:10.32628/CSEIT2410288

Authors

M. Naresh Babu Assistant Professor, Department of CSE - AI & ML, Sri Vasavi Institute of Engineering & Technology, Nandamuru, Andhra Pradesh, India Author
Akula Balakrishna Department of CSE - AI & ML, Sri Vasavi Institute of Engineering & Technology, Nandamuru, Andhra Pradesh, India Author
Galla Yasasri Department of CSE - AI & ML, Sri Vasavi Institute of Engineering & Technology, Nandamuru, Andhra Pradesh, India Author
Chalamalasetty Sri Siva Department of CSE - AI & ML, Sri Vasavi Institute of Engineering & Technology, Nandamuru, Andhra Pradesh, India Author
Pamarthi Vamsi Department of CSE - AI & ML, Sri Vasavi Institute of Engineering & Technology, Nandamuru, Andhra Pradesh, India Author

DOI:

https://doi.org/10.32628/CSEIT2410288

Keywords:

Brain Tumour, MRI, Classification, Support Vector Machine, Convolution Neural Network

Abstract

Tumour is the undesired mass in the body. Brain tumour is the significant growth of brain cells. Manual method of classifying is time consuming and can be done at selective diagnostic centers only. Brain tumour classification is crucial task to do since treatment is based on different location and size of it. Magnetic Resonance Imaging (MRI) is most suitable way to do so. Hence there is a need to build such system which will automatically classify the brain tumour type based on input MR images only. The objective of the proposed system isto classify the brain tumour images into three sub-types: Meningioma, Glioma and Pituitary using convolutional neural network (CNN) and Support vector machine (SVM). Images from the dataset are downsized to reduce computation and some salt noise is added to make model robust and increase the dataset. The performance comparison is done on Google Colab and tensorflow platform in python language.

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