Deep Feature Fusion Based Effective Brain Tumor Detection and Classification Approach Using MRI

Abstract

Brain tumors are a major global health issue, and their detection can be challenging. Typically, doctors visually inspect brain images to locate tumors, but this method is time-consuming and prone to errors. Recently, there has been progress in automated methods for early brain tumor diagnosis. However, challenges remain regarding limited precision and high false positive rate. Thus, an effective approach is needed for accurate tumor classification, utilizing the strong features of the tumor. This study presents a novel approach for brain tumor detection and classification using the fusion of deep features from MRI scans. Preprocessing, including cropping and resizing, eliminates irrelevant information from the brain MRI. After that, transfer learning models (DenseNet-121, InceptionV3, MobileNetV2) extract meaningful deep features, capturing complex patterns in MRI images for accurate tumor detection and classification. The extracted deep features are combined into a single feature vector and utilized as input for both a support vector machine (SVM) and a K-nearest neighbor (KNN) classifier for the final prediction. By combining the deep features into a unified feature vector, the model incorporates more information, resulting in improved classification performance. Two publicly available datasets are used to evaluate the effectiveness of the proposed approach, and the results demonstrate that the combined feature vector outperforms the individual feature vectors. Furthermore, the proposed approach demonstrated superior performance compared to existing methods, achieving the highest classification accuracy. This highlights its potential to support medical professionals in accurately detecting and classifying brain tumors.