Optimal brain tumor diagnosis based on deep learning and balanced sparrow search algorithm

In recent years, the diagnosis of brain tumors with the help of magnetic resonance imaging (MRI) methods has received significant attention. MRI techniques with substantial capabilities of displaying the internal structures of the human body have become one of the most widely used methods in this field. In the present study, a tumor is segmented after effectively preprocessing MRI images. Then, the main features are mined using a combination of the gray-level cooccurrence matrix and discrete wavelet transform. Finally, the mined features are fed into an optimized convolutional neural network (CNN)-based classification using a new improved metaheuristic technique, called balanced sparrow search algorithm (BSSA) for the final diagnosis to improve the efficiency of the CNN concerning consistency and accuracy. To verify the efficacy of the recommended algorithm, it is implemented on the whole brain atlas (WBA) database, and the results are compared with certain new and well-known methods. A comparative result also has been performed to the study, and the results show that the highest accuracy achieved by the recommended BSSA-CNN system is 93.65%. In addition, it is demonstrated that the specificity of 65.07% in the presented method yields results that are significantly better than those of the competing methods.