A Biologically Inspired ELM-based Framework for Classification of Brain MRIs

Use of medical images for clinical analysis of various critical diseases have become increasingly predominant in modern health care systems. Application of machine learning technique in this context evolves as a potential solution in terms providing faster output with high diagnostic accuracy. In this work we propose an Extreme Learning Machine (ELM) based classifier SFLA-ELM for detection of normal and pathological brain condition from brain Magnetic Resonance Images (MRIs). ELM is known for its speed and accuracy whereas the proposed method uses a swarm based evolutionary technique Shuffled Frog Leaping Algorithm (SFLA) and 10-fold cross validation method to optimally determine the network parameter of the ELM for better classification performance. The proposed model is experimented on three different brain MRI datasets of three different brain diseases. To get better approximation accuracy and generalization ability for the base ELM classifier, the suitable activation function and the appropriate number of hidden layer nodes are chosen. The performance validation of the proposed framework is done under two different network conditions, i.e. fixed network structure and varying network structure, by comparing its performance with two standard hybridized ELM classifiers, namely, PSO-ELM and ABC-ELM. The comparative performance analysis suggests that the proposed SFLS-ELM gives better classification performance in diagnosing the diseases in terms of accuracy, sensitivity, specificity, F-score and Area under ROC curve (AUC).Furthermore, the SFLA-ELM also found to offer better generalization ability and better stability with more compact network structure. A Biologically Inspired ELM-based Framework for Classification of Brain MRIs