Deep Learning and Optimized Learning Machine for Brain Tumor Classification

Brain Tumor classification in MRI images is a time-consuming and tedious task for medical professionals. An accurate classification model can assist healthcare providers in treating patients with effective care. In this research work, an enhanced learning machine for classifying brain tumors has presented for medical specialist's assistance. Deep learning architectures like Inception V3 and DenseNet201 are used to retrieve the categorization model's basic features. Along with the features collected using deep learning models, radiomic properties are integrated before classification in order to increase classification accuracy. Particle swarm optimized kernel Extreme Learning Machine (PSO-KELM) model has used to categorize the features into four groups like No Tumor, Gliomas, Meningiomas and Pituitary Tumors. Our system employs two benchmark datasets to evaluate the efficiency of the developed classification system using measures such as accuracy, recall, precision, falsepositive rate, recall, precision, f1 -score, and AUC ROC score, all of which our model performs better than the literature values. In addition, the four existing optimized learning methods are individually compared with dataset 1 and five approaches are independently evaluated with dataset 2. Accuracy measure is used to authenticate the improved performance analysis of oursuggestedsystem. In training and testing phases, the suggested model accomplishesimproved accuracy than theState-of-Art deep learning approaches. Our system's classification accuracy is 96.17% and 97.92% for datasets 1 and 2, respectively. Similar to the training method, the proposed testing model's accuracy is improved as 97.97% and 98.21%, respectively.