HYBRID FIREFLY META OPTIMIZATION FOR BIO MEDICAL IMAGE PROCESSING USING DEEP LEARNING

Abstract

Signal and image processing is a part of biomedical science. In that, Biomedical image processing have a great value such as recognition, segmentation and classification for disease diagnosis. In one part of biomedical science, brain tumor classification is considered with Magnetic Resonance Images (MRI) images using state of art models. Initially, the Convolutional Neural Network (CNN), Fast Convolutional Neural Network (FCNN), U-Net and M-Net model was considered for classification. Further, the Hybrid Firefly Meta Optimization (HFMO) is proposed for the better prediction purpose. The proposed work has three phases like normalization with augmentation, deep attention segmentation and classification. In the first phase, data augmentation is applied to increase the scope of the dataset. In the second phase, a deep attention technique is applied to concentrate on hotspot in the image during segmentation. Finally, a hybrid firefly optimization is applied to enhance the performance of the model in convolution neural network by backtracking the process. The measuring parameters like Dice coefficient, Jaccard index, Positive Projected Value (PPV), True Positive Rate and False Positive Rate were evaluated. The comparative analysis of various state of art models with proposed classifier were demonstrated. Thus the proposed technique produces the training accuracy as 98.62%, testing accuracy as 95.31 % and 1 % of loss.