Brain Tumour Detection and Classffication Using CNN

Neetu Nagar; Nishi  Pandey; Abhishek  Agwekar

Authors

Neetu Nagar Dept. of Electronics and Communication, Truba College of Science & Technology, Bhopal, MP, India https://orcid.org/0000-0001-5698-7068
Nishi Pandey Dept. of Electronics and Communication, Truba College of Science & Technology, Bhopal, MP, India
Abhishek Agwekar Dept. of Electronics and Communication, Truba College of Science & Technology, Bhopal, MP, India

Keywords:

Segmentation, Medical image, CNN, Machine learning, Classification, Barin tumour

Abstract

The goal of this study is to find brain tumours and give better care to those who have them. Tumours are abnormal growths of cells in the brain, and cancer is the word for a type of malignant tumour. Most of the time, CT or MRI scans are used to find areas of cancer in the brain. Positron Emission Tomography, Cerebral Arteriogram, Lumbar Puncture, and Molecular testing are also used to find brain tumours. In this study, the dataset has 253 Brain MRI images in two folders called "yes" and "no". The "yes" folder has 155 Brain MRI images of tumours, while the "no" folder has 98 Brain MRI non-tumour images. For classification and segmentation of preprocessing augmented data set we use novel CNN architecture in this paper we have achieved a Training accuracy is 98.75% and the validation accuracy is 86.45%. The novel CNN architecture could be used as a useful decision-support tool for radiologists in medical diagnostics.

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