Title: Classification of brain MRI using hypercolumn technique with convolutional neural network

Authors: K. Kavin Kumar

Addresses: Kongu Engineering College, Perundurai, Erode, Tamil Nadu, 638-060, India

Abstract: The purpose of this study is to classify brain tumours. When abnormal cells grow within the brain, a brain tumour develops. Cancerous (malignant) tumours and benign (non-cancerous) tumours are the two basic forms of tumours. In this regards an algorithm is developed to classify the tumour and non-tumour. The main focus is given to the hypercolumn implementation along with VGG-16 and ALEX-NET. The datasets were taken from the Kaggle and real brain data from Johnson's MRI and it consists of tumour and non-tumour. The obtained result identifies whether the person is having a tumour or not. The developed algorithm is having an accuracy of 94.5% for VGG-16 with hypercolumn and 91.2% for ALEXNET with hypercolumn.

Keywords: tumour; non-tumour; hypercolumn; ALEXNET; VGG16; MRI; malignant; benign.

DOI: 10.1504/IJMEI.2024.140810

International Journal of Medical Engineering and Informatics, 2024 Vol.16 No.5, pp.455 - 465

Received: 24 Dec 2021
Accepted: 08 Feb 2022
Published online: 03 Sep 2024 *

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