PMID- 31812927
OWN - NLM
STAT- MEDLINE
DCOM- 20210125
LR  - 20210125
IS  - 1532-2777 (Electronic)
IS  - 0306-9877 (Linking)
VI  - 136
DP  - 2020 Mar
TI  - 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.
PG  - 109507
LID - S0306-9877(19)31095-3 [pii]
LID - 10.1016/j.mehy.2019.109507 [doi]
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.
CI  - Copyright (c) 2019 Elsevier Ltd. All rights reserved.
FAU - Sisik, Fatih
AU  - Sisik F
AD  - Goksun Vocational School, Department of Computer Programming, Kahramanmaras Sutcu 
      Imam University, K.Maras, Turkey.
FAU - Sert, Eser
AU  - Sert E
AD  - Department of Computer Engineering, Engineering and Architecture Faculty, 
      Kahramanmaras Sutcu Imam University, K.Maras, Turkey. Electronic address: 
      esersert@ksu.edu.tr.
LA  - eng
PT  - Journal Article
DEP - 20191118
PL  - United States
TA  - Med Hypotheses
JT  - Medical hypotheses
JID - 7505668
SB  - IM
MH  - Algorithms
MH  - Brain/diagnostic imaging
MH  - Brain Neoplasms/*diagnosis
MH  - *Cluster Analysis
MH  - Diagnosis, Computer-Assisted/*instrumentation/*methods
MH  - *Fuzzy Logic
MH  - Glioblastoma/*diagnosis
MH  - Humans
MH  - Image Processing, Computer-Assisted/methods
MH  - Magnetic Resonance Imaging
MH  - Neurons/metabolism
MH  - Software
MH  - Support Vector Machine
OTO - NOTNLM
OT  - Brain tumor segmentation
OT  - Extreme learning machine
OT  - Magnetic resonance imaging
OT  - Raspberry Pi
OT  - Segmentation
OT  - Significantly fast and robust fuzzy C-means clustering
COIS- Declaration of Competing Interest The authors declare that they have no known 
      competing financial interests or personal relationships that could have appeared 
      to influence the work reported in this paper.
EDAT- 2019/12/10 06:00
MHDA- 2021/01/26 06:00
CRDT- 2019/12/09 06:00
PHST- 2019/09/30 00:00 [received]
PHST- 2019/11/11 00:00 [revised]
PHST- 2019/11/16 00:00 [accepted]
PHST- 2019/12/10 06:00 [pubmed]
PHST- 2021/01/26 06:00 [medline]
PHST- 2019/12/09 06:00 [entrez]
AID - S0306-9877(19)31095-3 [pii]
AID - 10.1016/j.mehy.2019.109507 [doi]
PST - ppublish
SO  - Med Hypotheses. 2020 Mar;136:109507. doi: 10.1016/j.mehy.2019.109507. Epub 2019 
      Nov 18.