Brain tumor segmentation approach based on the extreme learning machine and significantly fast and robust fuzzy C-means clustering algorithms running on Raspberry Pi hardware.
Automatic decision support systems have gained importance in health sector in recent years. In parallel with recent developments in the fields of artificial intelligence and image processing, embedded systems are also used in decision support systems for tumor diagnosis. Extreme learning machine (EL...
| Publicado en: | Medical Hypotheses Vol. 136 |
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| Autores principales: | , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Elsevier B.V.
Mar2020
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=141734975&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 141734975 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 03069877 NZO jtl: Medical Hypotheses issn: 03069877 maglogo: N pubinfo: dt: Mar2020 vid: 136 pid: 467 pub: Elsevier B.V. place: New York, New York artinfo: ui: 141734975 141734975 NLM31812927 141734975 10.1016/j.mehy.2019.109507 NLM31812927 141734975 ppct: 1 formats: tig: atl: Brain tumor segmentation approach based on the extreme learning machine and significantly fast and robust fuzzy C-means clustering algorithms running on Raspberry Pi hardware. aug: au: ŞİŞİK, Fatih SERT, Eser Şişik, Fatih affil: Göksun Vocational School, Department of Computer Programming, Kahramanmaras Sutcu Imam University, K.Maras, Turkey sug: subj: Diagnosis, Computer Assisted Equipment and Supplies Glioma Diagnosis Logic Diagnosis, Computer Assisted Methods Brain Neoplasms Diagnosis Clustering Algorithms Utilization Extreme Learning Machines Image Processing, Computer Assisted Methods Software Magnetic Resonance Imaging Brain Neurons Metabolism Human ab: Automatic decision support systems have gained importance in health sector in recent years. In parallel with recent developments in the fields of artificial intelligence and image processing, embedded systems are also used in decision support systems for tumor diagnosis. Extreme learning machine (ELM), is a recently developed, quick and efficient algorithm which can quickly and flawlessly diagnose tumors using machine learning techniques. Similarly, significantly fast and robust fuzzy C-means clustering algorithm (FRFCM) is a novel and fast algorithm which can display a high performance. In the present study, a brain tumor segmentation approach is proposed based on extreme learning machine and significantly fast and robust fuzzy C-means clustering algorithms (BTS-ELM-FRFCM) running on Raspberry Pi (PRI) hardware. The present study mainly aims to introduce a new segmentation system hardware containing new algorithms and offering a high level of accuracy the health sector. PRI's are useful mobile devices due to their cost-effectiveness and satisfying hardware. 3200 training images were used to train ELM in the present study. 20 pieces of MRI images were used for testing process. Figure of merid (FOM), Jaccard similarity coefficient (JSC) and Dice indexes were used in order to evaluate the performance of the proposed approach. In addition, the proposed method was compared with brain tumor segmentation based on support vector machine (BTS-SVM), brain tumor segmentation based on fuzzy C-means (BTS-FCM) and brain tumor segmentation based on self-organizing maps and k-means (BTS-SOM). The statistical analysis on FOM, JSC and Dice results obtained using four different approaches indicated that BTS-ELM-FRFCM displayed the highest performance. Thus, it can be concluded that the embedded system designed in the present study can perform brain tumor segmentation with a high accuracy rate. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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