Adaptive fuzzy deformable fusion and optimized CNN with ensemble classification for automated brain tumor diagnosis.

Automatic classification of brain tumor plays a vital role to speed up the treatment procedure, plan and boost the survival rate of patients. Nowadays, Magnetic Resonance Imaging (MRI) is employed for determining brain tumor. However, manual identification of brain tumor is purely based on the sensitivity and experience of medical professionals. Thus, more research works towards brain tumor classification have been implemented for minimizing the human factor. Different imaging approaches are employed for detecting brain tumors. Though, MRI is mainly employed owing to the better quality of images due to the non ionizing radiation of images. One of the major categories of machine learning is called deep learning, which shows an outstanding performance, mainly on solving the segmentation and classification issues. The aim of this paper to introduce a new brain tumor classification model based on the intelligent segmentation and classification approaches. The main phases of the proposed model are (a) Data collection, (b) Pre-processing, (c) Tumor segmentation, and (d) Tumor Classification. Initially, the datasets related to the brain tumor are gathered from several benchmark sources and subjected to the pre-processing step. Here, it is performed by the median filtering and contrast enhancement techniques. The first contribution of this paper is the development of an enhanced segmentation approach termed as Adaptive Fuzzy Deformable Fusion (AFDF)-based Segmentation, which merges the two concepts of Fuzzy C-Means Clustering (FCM) and snake deformable approach. Here, the significant parameters of the AFDF are optimized by the improved Deer Hunting Optimization Algorithm (DHOA) termed Adaptive Coefficient Vector-based DHOA (ACV-DHOA). The classification of images is performed by the Optimized Convolutional Neural Network with Ensemble Classification (OCNN-EC) after segmenting the tumor. In the proposed deep learning classification, the number of convolutional layers and hidden neurons of CNN is optimized by the ACV-DHOA, and the fully connected layer is replaced by the ensemble classifier with Deep Neural Network (DNN), autoencoder, and Support Vector Machine (SVM). The classifier which is getting high rank is considered as the optimal one. The experimentation results are performed on the standard database that shows the high classification accuracy of the developed model by evaluating with other conventional methods.