A deep learning approach for brain tumor classification using MRI images

Brain tumors can be fatal if not detected early enough. Manually diagnosing brain tumors requires the radiologist's experience and expertise, which may not always be available. Furthermore, manual processes are inefficient, prone to errors, and time-taking. Therefore, an effective solution is required to ensure an accurate diagnosis. To this end, we propose an automated technique for detecting brain tumors using magnetic resonance imaging (MRI). First, brain MRI images are pre-processed to enhance visual quality. Second, we apply two different pre-trained deep learning models to extract powerful features from images. The resulting feature vectors are then combined to form a hybrid feature vector using the partial least squares (PLS) method. Third, the top tumor locations are revealed via agglomerative clustering. Finally, these proposals are aligned to a predetermined size and then relayed to the head network for classification. Compared to existing approaches, the proposed method achieved a classification accuracy of 98.95%.