Deep Learning-Based Multi-Layers Brain Tumor Detection and Classification from MRI Images
DOI:
https://doi.org/10.65204/djes.v3i1.450Abstract
Background: According to American Cancer Society, brain tumor represents a highly feared disease in medical science, and it is among the most prevalent malignant tumors to be detected in people of different ages. Treatment of brain tumors requires early and accurate detection of the disease.
Methodology: With using machine learning and deep features classifiers, this paper describes the classification of brain tumors. Using a set of pre-trained deep convolutional neural networks (DCNNs), deep features were extracted from a collection of brain magnetic resonance imaging (MR) images by incorporating the concept of translational learning (TL) into our proposed framework. After that, a number of machine learning (ML) classifiers assess the deep features that were recovered. Deep features are used for defining and sequencing several ML feature classifiers. For the prediction of final output, these are after that input into a number of ML classifiers. In this study, Three publicly available brain MRI datasets were used to evaluate the effectiveness of different types of pre-trained models, including machine learning classifiers and deep feature extractors, in classifying brain tumors.
Results: The experimental results show that deep feature extractors significantly improved performance levels, and in most cases, a convolutional neural network (CNN) was used. This study achieved an accuracy of 0.98% and an accuracy of 0.99% for the valves.
References
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