Open AccessBrain Tumor Detection Using Deep Learning
Author(s) -
Sufiyan Salim Akbani,
Adeeba Naaz,
Nazish Kausar,
Prof. Abdul Razzaque
Publication year - 2022
Publication title -
international journal for research in applied science and engineering technology
Language(s) - English
Resource type - Journals
ISSN - 2321-9653
DOI - 10.22214/ijraset.2022.41321
Subject(s) - convolutional neural network , computer science , transfer of learning , deep learning , artificial intelligence , preprocessor , brain tumor , pattern recognition (psychology) , contextual image classification , task (project management) , medical imaging , magnetic resonance imaging , machine learning , image (mathematics) , radiology , pathology , medicine , management , economics
One of the most leading death causes in the world is brain tumor. Tumor Detection is one of the most difficult tasks in medical image processing. In fact, the manual classification with human-assisted support can be improper prediction and diagnosis shown by medical evidence. The detection task is too difficult to perform because there is a lot of diversity in the images as brain tumors come in different shapes and textures. Recently, deep learning techniques showed promising results towards improving accuracy of detection and classification of brain tumor from magnetic resonance imaging (MRI). In this paper, we propose a deep learning model for the classification of brain tumors from MRI images using convolutional neural network (CNN) based on transfer learning. The implemented system explores a number of CNN architectures, image preprocessing and transfer learning model named MobilNet to achieve the better performance and accuracy. Keywords: Deep learning, convolutional neural network, Transfer learning, Brain tumor, medical image classification, MobileNet architecture, etc.