Hybrid Feature-based Machine Learning Framework for Automated Brain Tumor Classification
Abstract
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The classification of brain tumors using Magnetic Resonance Imaging (MRI) is crucial for early diagnosis and efficient decision-making in clinical practice. However, manual analysis is a time-consuming process that may introduce observer bias. To address these issues, the current study presented a hybrid feature-based Machine Learning (ML) model for automated brain tumor classification, integrating both handcrafted and Deep Learning (DL) features to enhance robustness and accuracy. The suggested method combines Histogram of Oriented Gradients (HOG) to retrieve local texture data and high-level semantic details obtained with the help of the ResNet-50 deep convolutional neural network (CNN). The hybrid feature vectors are then classified with various ML classifiers, such as Linear, Gaussian, and Quadratic Support Vector Machines (SVMs), Logistic Regression (LR), Random Forest (RF), and XGBoost. The model was tested using two publicly-available benchmark datasets, viz., the Figshare Brain Tumor MRI dataset and Harvard Brain Tumor MRI dataset of a total of 10,286 MRI images. The experimental findings showed that the hybrid framework is best in the Figshare dataset, where Quadratic SVM has the best classification accuracy of 97%, and the other models are 96% and 95% with Gaussian SVM and LR, respectively. RF yields optimal accuracy on the more difficult Harvard dataset at 76%, which means that the proposed approach can be generalized to diverse data distributions. A further study is an ablation study, which supports the claim that the combination of handcrafted and deep features is a significant performance metric on classification when compared to the features separately. The findings confirmed that the hybrid framework has been developed as a suitable, robust, and universalized framework to classify automated multi-class brain tumors with the help of MRI images.
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