Deep Learning and Optimized Learning Machine for Brain Tumor Classification.

[Display omitted] • There are a lot of abnormalities in the sizes and location of the brain tumor(s). This makes it really difficult for complete understanding of the nature of the tumor. • Often times in developing countries the lack of skillful doctors and lack of knowledge about tumors makes it really challenging and time-consuming to generate reports from MRI. • A novel feature extraction method to extract the basic features using deep learning models and radiomic features of 93 using pyradiomics to understand the nature of the tumor and classify it accordingly. • In the literature limited number of MRI images was used, in our work two benchmark datasets are utilized to improve the accuracy of the recommended model. • Novel filtering techniques, improved preprocessing methods and an enhanced hybrid optimized deep learning machine was developed are used to improve the efficiency of the suggested model. • Preprocessing using literature algorithm is complex and time consuming for noise removal. • The state-of-art depends on the Convolution operation for feature gathering, in our system a novel feature fusion was initiated to analyze the nature of the tumor for accurate classification. • A novel deep learning classifier was developed to classify the brain tumors into four specified categories. • The classification performance was measured using eight performance metrics. • To validate the performance of our proposed system, it was compared with the existing deep learning approaches stated in the paper with respect to the accuracy parameter. • The overall performance and comparison of our system outperforms the literature Brain Tumor Classification and Brain Tumor (MRI) datasets. 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, false-positive 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. [ABSTRACT FROM AUTHOR]