Supervised meta-heuristic extreme learning machine for multiple sclerosis detection based on multiple feature descriptor
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Supervised meta‑heuristic extreme learning machine for multiple sclerosis detection based on multiple feature descriptors in MR images Adele Rezaee1 · Khosro Rezaee2 · Javad Haddadnia3 · Hamed Taheri Gorji4 Received: 3 November 2019 / Accepted: 5 April 2020 © Springer Nature Switzerland AG 2020
Abstract In this study, we propose a hybrid approach involving feature extraction, feature selection, and optimized learning for the diagnosis of multiple sclerosis (MS), which can detect the lesion caused by MS plaques in the brain using magnetic resonance imaging analysis. A major challenge associated with lesion diagnosis by neurologists is that it is a timeconsuming process and demands high expertise; therefore, researchers have been stimulated to find an auto-diagnose method of the disease. Given the high resemblance of MS plaque-induced lesions and other lesions such as Alzheimer’s or dementia, scant research has explored the diagnosis of MS-induced lesions, most of which suffering from the lack of an efficient and accurate method. Informed by the need for a precise hybrid model for the classification of MS plaques and other comparable lesions, a solution is proposed that utilizes an efficient model. In this method, after image preprocessing, the feature vector is created by applying fractal and Pseudo-Zernike Moments descriptors. Feature selection using the Difference Evolution) algorithm to select the minimum subset of features will reduce the number of Extreme Learning Machine (ELM) inputs for classification. To improve the classification effect, the ELM wavelet kernel parameters are also regulated by the Shuffled Frog-Leaping Algorithm. By applying the proposed model to a set of brain MR images obtained from healthy subjects and MS patients during different experimental iterations, an average accuracy of 97% was obtained. The results of the method were estimated under specific conditions, and finally the proposed model yielded desirable outputs compared to similar methods. Keywords Multiple sclerosis · Extreme learning machine · Shuffled frog-leaping algorithm · Differential evolution · Fractal descriptor
1 Introduction Multiple Sclerosis is an inflammatory disease in which myelin sheaths of the nerve cells in the brain and spinal cord are damaged [1–6]. This damage can disrupt the ability of parts of the nervous system which are responsible for communication and causes many signs and symptoms such as physical problems [7]. Regional estimates suggest that this disease has a moderate prevalence in countries and is in prevalence range of the European and Far East
countries [8]. Symptoms of MS disease appear in several forms and its new symptoms occur either as step recurrence -multiple disease reversal or alternately over time [9–11]. Initially, recovery from attacks is almost complete, but slowly, neuropsychiatric disabilities with different degrees will remain from each attack [12]. MRI is the most practical method for detecting the masses left in the brain, which can greatly help the specialist. Sometimes th
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