Efficient Brain Tumor Classification with a Hybrid CNN-SVM Approach in MRI

Brain Magnetic Resonance Imaging (MRI) is a crucial diagnostic tool in neuroimaging that provides valuable insights into various neurological disorders. Accurate classification of brain MRI images is vital in aiding medical professionals in diagnosis and treatment planning. The multiclass classification of brain MRI images has significant implications in clinical practice. Accurate classification can aid in detecting and characterizing various brain abnormalities, including tumors, haemorrhages, and neurological disorders. Our suggested strategy can help doctors make prompt and accurate diagnoses by automating the classification process and improving patient care and results. This study uses the two standard datasets, Brats and Sartaj, to propose a thorough method for multiclass classification of brain MRI utilizing Convolutional Neural Network (CNN), VGG19, and the Convolutional Neural Network-Support Vector Machines (CNN-SVM) algorithm. The proposed approach leverages the power of deep learning for feature extraction and the versatility of Support Vector Machines (SVM) for classification. Firstly, the CNN model is trained to extract discriminative features from brain MRI images. The VGG19 architecture, a widely used pre-trained CNN, is employed as a feature extractor. By utilizing the pre-trained weights of VGG19, the model can effectively capture high-level representations of the input images. The results demonstrate the efficacy of this method in accurately classifying brain MRI images. Further research can explore the application of this approach in larger datasets and investigate other deep learning architectures for feature extraction, providing further advancements in medical image analysis and diagnosis.