Year: 2024
Type: article
Abstract: Early identification of brain tumors significantly increases the possibility of successful treatment and improves patient outcomes.Traditional diagnostic methods often involve manual examination of MRI scans by radiologists, which can be both time-consuming and result in errors.In recent years, machine learning techniques have shown promise in automating and enhancing the accuracy of medical image analysis.This paper presents a comparative study of two machine learning algorithms-Convolutional Neural Networks (CNN) and Support Vector Machines (SVM)-for the detection of brain tumors from MRI scans.Our research utilized a dataset that is available publicly of brain MRI images, which were preprocessed to improve the quality and consistency of the data.The CNN model was designed with multiple convolutional layers, using ReLU activation functions and max-pooling layers, and trained with the Adam optimizer.The SVM model employed a feature extraction process followed by kernel-based classification.Both models were evaluated using a comprehensive set of performance metrics, including accuracy, precision, recall, and F1-score.The results indicated that the CNN model outperformed the SVM model in terms of accuracy and robustness, achieving a higher detection rate of brain tumors.
Language: English
Subfield: Neurology
Field: Neuroscience
Domain: Life Sciences
Open Access status: hybrid
OpenAlex ID: W4401863306