Optimizing Brain Tumor Detection: Enhancing Diagnostic Accuracy in Brain Tumor Detection Using A Hybrid Approach of Machine Learning and Deep Learning Models

Brain tumor detection and classification are critical tasks in medical imaging, requiring both precision and efficiency. Traditional diagnostic techniques, though effective, often require substantial time and expertise. This study explores the application of a wide array of machine learning and deep learning models, including Convolutional Neural Networks (CNN), DenseNet121, Random Forest, Support Vector Machine (SVM), and Naive Bayes, to automate and enhance brain tumor detection and classification. We utilized multiple datasets, including MRI scans, to evaluate these models, employing preprocessing techniques such as image resizing, normalization, and standardization to improve accuracy. The models were evaluated based on performance metrics such as accuracy, precision, recall, and F1 score. Our results demonstrate that CNN models excel in image-based tumor classification with a training accuracy of 99%, while Naive Bayes and Random Forest classifiers achieved perfect accuracy on structured datasets. This comprehensive evaluation of machine learning methodologies shows the potential of automated diagnostic systems to improve early detection and treatment planning for brain tumor patients.