Brain Tumor Detection From MRI Images Using Deep Learning Techniques

Author:

Sharma Anu1ORCID

Affiliation:

1. Moradabad Institute of Technology, Moradabad, India

Abstract

Machine learning and deep learning algorithms are utilized to identify brain tumors in a number of research papers. When these algorithms are applied to MRI images, it takes exceedingly slight time to expect a brain tumor, and the increased accuracy makes it easier to treat patients. The performance of the hybrid Convolution Neural Network (CNN) used in the proposed work to detect the existence of brain tumours is examined. In this study, we suggested a hybrid convolutional neural network followed by deep learning techniques using 2D magnetic resonance brain pictures, segment brain tumors (MRI). In our research, hybrid CNN achieved an accuracy of 98.73%, outperforming the results so far.

Publisher

IGI Global

Reference19 articles.

1. Brain Tumor Detection and Classification on MR Images by a Deep Wavelet Auto-Encoder Model

2. Anu, S. (2017). Literature Review and Challenges of Data Mining Techniques for Social Network Analysis. journal Advances in Computational Sciences and Technology, 10(5)

3. Anu, S. (2019). Hybrid Neuro-Fuzzy Classification Algorithm for Social Network. International Journal of Engineering and Advanced Technology, 8(6).

4. Ariful, I. (2020). Brain Tumor Detection from MRI Images using Image Processing. International Journal of Innovative Technology and Exploring Engineering (IJITEE), 9(8).

5. Image Analysis for MRI Based Brain Tumor Detection and Feature Extraction Using Biologically Inspired BWT and SVM

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