A Hybrid Model of Feature Extraction and Dimensionality Reduction Using ViT, PCA, and Random Forest for...

Background/Objectives: The brain serves as the central command center for the nervous system in the human body and is made up of nerve cells known as neurons. When these nerve cells grow rapidly and abnormally, it can lead to the development of a brain tumor. Brain tumors are severe conditions that can significantly reduce a person's lifespan. Failure to detect or delayed diagnosis of brain tumors can have fatal consequences. Accurately identifying and classifying brain tumors poses a considerable challenge for medical professionals, especially in terms of diagnosing and treating them using medical imaging analysis. Errors in diagnosing brain tumors can significantly impact a person's life expectancy. Magnetic Resonance Imaging (MRI) is highly effective in early detection, diagnosis, and classification of brain cancers due to its advanced imaging abilities for soft tissues. However, manual examination of brain MRI scans is prone to errors and heavily depends on radiologists' experience and fatigue levels. Swift detection of brain tumors is crucial for ensuring patient safety. Methods: In recent years, computer-aided diagnosis (CAD) systems incorporating deep learning (DL) and machine learning (ML) technologies have gained popularity as they offer precise predictive outcomes based on MRI images using advanced computer vision techniques. This article introduces a novel hybrid CAD approach named ViT-PCA-RF, which integrates Vision Transformer (ViT) and Principal Component Analysis (PCA) with Random Forest (RF) for brain tumor classification, providing a new method in the field. ViT was employed for feature extraction, PCA for feature dimension reduction, and RF for brain tumor classification. The proposed ViT-PCA-RF model helps detect early brain tumors, enabling timely intervention, better patient outcomes, and streamlining the diagnostic process, reducing patient time and costs. Our research trained and tested on the Brain Tumor MRI (BTM) dataset for multi-classification of brain tumors. The BTM dataset was preprocessed using resizing and normalization methods to ensure consistent input. Subsequently, our innovative model was compared against traditional classifiers, showcasing impressive performance metrics. Results: It exhibited outstanding accuracy, specificity, precision, recall, and F1 score with rates of 99%, 99.4%, 98.1%, 98.1%, and 98.1%, respectively. Conclusions: Our innovative classifier's evaluation underlined our model's potential, which leverages ViT, PCA, and RF techniques, showing promise in the precise and effective detection of brain tumors.