A Comparative analysis of Advanced Machine Learning Techniques for Enhancing Brain Tumor Detection

Abstract

Detection and classification of brain tumors are prominent tasks in neuroimaging, as it ensures accurate and timely diagnosis for effective treatment planning. This study explores the utility of machine learning models, such as convolutional neural networks, VGG 16 and 19 architectures, and recurrent neural networks, in improving the detection of brain tumor anomaly in both CT and MRI. A total of 3400 images were integrated and collected from multiple sources and prepared for usage by conducting meticulous preprocessing and feature extraction. Following the preparation process, the models were trained using stratified dataset split with a 70/30 ratio. The testing results indicated that the VGG 16 and VGG 19 architectures yielded the highest performance results, as they produced the optimal precision, recall, and F1-score values that reached up to 96.7%, 97.2%, and 96.5%, respectively, along with the highest AUC-ROC scores. In comparison, the CNN and RNN models presented lower performance results in each measured metric. The superiority of the VGG architectures strengthes the idea that the complexity and the capacity of the model to memorize and retain the imaging features is crucial for the accurate detection of the tumors. As such, the improved results can be helpful for healthcare professionals, as they provide powerful tools to ensure precise detection and characteristics of brain tumors. Nevertheless, further research is required to validate the results with a larger dataset, and the adherence of the study to the black-box nature of the model poses limitations on the interpretation. In any case, the study supplements the growing body of research in medical imaging and contributes to the prevention and management of brain tumors.