A Hybrid Deep Model for Brain Tumor Classification

Classification of brain tumors from Magnetic Resonance Images (MRIs) using Computer-Aided Diagnosis (CAD) has faced some major challenges. Diagnosis of brain tumors such as glioma, meningioma, and pituitary mostly rely on manual evaluation by neuro-radiologists and is prone to human error and subjectivity. In recent years, Machine Learning (ML) techniques have been used to improve the accuracy of tumor diagnosis with the expense of intensive pre-processing and computational cost. Therefore, this work proposed a hybrid Convolutional Neural Network (CNN) (i.e., AlexNet followed by SqueezeNet) to extract quality tumor biomarkers for better performance of the CAD system using brain tumor MRI’s. The features extracted using AlexNet and SqueezeNet are fused to preserve the most important biomarkers in a computationally efficient manner. A total of 3064 brain tumors (708 Meningioma, 1426 Glioma, and 930 Pituitaries) MRIs have been experimented. The proposed model is evaluated using several well-known metrics, i.e., Overall accuracy (94%), Precision (92%), Recall (95%), and F1 score (93%) and outperformed many state of the art hybrid methods.