Detection of Brain Tumors Using Transfer Learning Features and Machine Learning Techniques
S. Sathishkumar, Janani, Kiruba, Harshitha, Dreghaa, Giftlin Priyanka
2D Magnetic Resonance brain Images (MRI), Convolutional Neural Network, Wiener filter, Fuzzy c-means.
This paper proposes a Magnetic Resonance Imaging (MRI) based brain tumor image detection using ConvolutionalNeural Network (CNN) based deep learning method. In modern days, checking the huge number of MRI images and finding a brain tumor manually by a human is a very tedious and inaccurate task. It affects the proper medical treatment of the patient. Again, it is hugely time-consuming task as it involves a huge number of image datasets. The proposed an algorithm to segment brain tumors from 2D MRI by a convolutional neural network which is followed by traditional classifiers and deep learning methods and Transfer learning method for feature extraction. A CNN based model will help the doctors to detect brain tumors in MRI images accurately, so that the speed in treatment will increase a lot.
Article Details
Unique Paper ID: 158744

Publication Volume & Issue: Volume 9, Issue 10

Page(s): 632 - 635
Article Preview & Download

Share This Article

Join our RMS

Conference Alert

NCSEM 2024

National Conference on Sustainable Engineering and Management - 2024

Last Date: 15th March 2024

Call For Paper

Volume 11 Issue 1

Last Date for paper submitting for Latest Issue is 25 June 2024

About Us enables door in research by providing high quality research articles in open access market.

Send us any query related to your research on

Social Media

Google Verified Reviews