Deep learning techniques for classification of brain MRI
by Imayanmosha Wahlang; Pallabi Sharma; Sugata Sanyal; Goutam Saha; Arnab Kumar Maji
International Journal of Intelligent Systems Technologies and Applications (IJISTA), Vol. 19, No. 6, 2020

Abstract: Several brain diseases are becoming a threat to the livelihood of people. One such problem is the presence of a brain tumour. A brain tumour can be benign or malignant. It is dangerous if it is a malignant or secondary tumour (metastasis). Therefore, there is a need to detect the presence of tumours at the earliest stage as possible. Using an automated method for brain tumour detection can be a solution to medical expertise as a biopsy can be excluded if early detection could be possible. Classification helps in the prediction of the type of image and type of tumour. In this paper, three stages are involved. In the first stage, the classification of brain MR images into normal (tumour) or abnormal (non-tumour) images using ConvNet, LeNet, ResNet, and DenseNet has been analysed. In the second stage, architectures like LeNet and AlexNet are used in the prediction of the type of tumour namely metastasis, glioma, and meningioma. And lastly, using U-Net and AlexNet, classification into high grade glioma and low grade glioma was done.

Online publication date: Fri, 15-Jan-2021

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