Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{158744,
        author = {S. Sathishkumar and Janani and Kiruba and Harshitha and Dreghaa and Giftlin Priyanka},
        title = {Detection of Brain Tumors Using Transfer Learning Features and Machine Learning Techniques},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {9},
        number = {10},
        pages = {632-635},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=158744},
        abstract = {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.},
        keywords = {2D Magnetic Resonance brain Images (MRI), Convolutional Neural Network, Wiener filter, Fuzzy c-means.},
        month = {},
        }