Abstract
<span style="font-size: 11.0pt; line-height: 107%; font-family: 'Times New Roman',serif; mso-fareast-font-family: Calibri; mso-fareast-theme-font: minor-latin; mso-ansi-language: EN-IN; mso-fareast-language: EN-US; mso-bidi-language: AR-SA;">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.</span><span style="font-size: 11.0pt; line-height: 107%; font-family: 'Times New Roman',serif; mso-fareast-font-family: 'Droid Sans Fallback'; mso-ansi-language: EN-IN; mso-fareast-language: EN-US; mso-bidi-language: AR-SA;"> The proposed model is experimented on three different brain MRI datasets of three different brain diseases. </span><span style="font-size: 11.0pt; line-height: 107%; font-family: 'Times New Roman',serif; mso-fareast-font-family: Calibri; mso-fareast-theme-font: minor-latin; mso-ansi-language: EN-IN; mso-fareast-language: EN-US; mso-bidi-language: AR-SA;">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.</span><span style="font-size: 22.0pt; mso-bidi-font-size: 16.0pt; line-height: 107%; font-family: 'Times New Roman',serif; mso-fareast-font-family: Calibri; mso-fareast-theme-font: minor-latin; mso-ansi-language: EN-IN; mso-fareast-language: EN-US; mso-bidi-language: AR-SA;">A Biologically Inspired ELM-based Framework for Classification of Brain MRIs</span>
Highlights
Detection of normal and diseased brain conditions from Magnetic Resonance imaging (MRI) with high precision and accuracy has been a challenge for the health care professionals
C) The Receiver Operating Characteristics (ROC) curve analysis of the three hybrid methods shows that the curve for Shuffled Frog Leaping Algorithm (SFLA)-Extreme Learning Machine (ELM) is superior to ABC-ELM and PSO-ELM
D) SFLA-ELM achieves better sensitivity and specificity than the other two hybrid methods and thereby making it evident that, the proposed SFLA-ELM method is more appropriate for diagnosis of brain diseases from MRI data
Summary
Detection of normal and diseased brain conditions from Magnetic Resonance imaging (MRI) with high precision and accuracy has been a challenge for the health care professionals. In spite of the knowledge and experience, the human vision system restricts the manual interpretation and analysis of MR images for the clinical experts. This is due to the large volume of information contained in an MR image which is hard to interpret by human vision [12] This is the reason why use of automated image analysis methods utilizing machine learning and image processing techniques are of wide use in recent years in the field of MR image processing. These computers assisted diagnosis (CAD) techniques reduce burden on the radiologist and neurologists and improve the accuracy and objectivity of diagnosis [3,4,5,6,7]
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