Deep Learning-Based Brain Tumor Classification Prototype Using Transfer Learning

Accumulation of cells with unimpeded growth is the hallmark for the development of life challenging brain tumor disease. In pre-existing research Machine Learning analytical models are trained on domain specific dataset to achieve goals of an Artificial Intelligence based application in Computer Sci...

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Bibliographic Details
Published in2023 International Conference on Advances in Intelligent Computing and Applications (AICAPS) pp. 1 - 7
Main Authors Saju, Binju, Thomas, Laiby, Varghese, Fredy, Prasad, Arpana, Tressa, Neethu
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.02.2023
Subjects
Analytical models
Brain modeling
Brain tumor
Computational modeling
Data models
Glioma
Machine learning algorithms
Meningioma
Pituitary RESTNET 5.0
Transfer learning
Transfer Learning Model
Tumors
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Summary:Accumulation of cells with unimpeded growth is the hallmark for the development of life challenging brain tumor disease. In pre-existing research Machine Learning analytical models are trained on domain specific dataset to achieve goals of an Artificial Intelligence based application in Computer Science for the said disease identification. An ongoing research in the field is presented in this paper where an experimental set of 7038 domain specific images are used to train a model. On experiments conducted on the dataset using six different Machine Learning algorithms the researchers are able to identify Glioma tumor, Meningioma tumors and Pituitary tumor with an accuracy of 96% using RESTNET 5.0 with Transfer Learning Model.
DOI:10.1109/AICAPS57044.2023.10074201