PMID- 35795732
OWN - NLM
STAT- MEDLINE
DCOM- 20220708
LR  - 20220716
IS  - 1687-5273 (Electronic)
IS  - 1687-5265 (Print)
VI  - 2022
DP  - 2022
TI  - CNN-Based Brain Tumor Detection Model Using Local Binary Pattern and Multilayered 
      SVM Classifier.
PG  - 9015778
LID - 10.1155/2022/9015778 [doi]
LID - 9015778
AB  - In this paper, an autonomous brain tumor segmentation and detection model is 
      developed utilizing a convolutional neural network technique that included a 
      local binary pattern and a multilayered support vector machine. The detection and 
      classification of brain tumors are a key feature in order to aid physicians; an 
      intelligent system must be designed with less manual work and more automated 
      operations in mind. The collected images are then processed using image filtering 
      techniques, followed by image intensity normalization, before proceeding to the 
      patch extraction stage, which results in patch extracted images. During feature 
      extraction, the RGB image is converted to a binary image by grayscale conversion 
      via the colormap process, and this process is then completed by the local binary 
      pattern (LBP). To extract feature information, a convolutional network can be 
      utilized, while to detect objects, a multilayered support vector machine (ML-SVM) 
      can be employed. CNN is a popular deep learning algorithm that is utilized in a 
      wide variety of engineering applications. Finally, the classification approach 
      used in this work aids in determining the presence or absence of a brain tumor. 
      To conduct the comparison, the entire work is tested against existing procedures 
      and the proposed approach using critical metrics such as dice similarity 
      coefficient (DSC), Jaccard similarity index (JSI), sensitivity (SE), accuracy 
      (ACC), specificity (SP), and precision (PR).
CI  - Copyright (c) 2022 Morarjee Kolla et al.
FAU - Kolla, Morarjee
AU  - Kolla M
AD  - Department of Computer Science and Engineering, Chaitanya Bharthi Institute of 
      Technology, Hyderabad, Telangana, India.
FAU - Mishra, Rupesh Kumar
AU  - Mishra RK
AD  - Department of Computer Science and Engineering, Chaitanya Bharthi Institute of 
      Technology, Hyderabad, Telangana, India.
FAU - Zahoor Ul Huq, S
AU  - Zahoor Ul Huq S
AD  - Department of Computer Science and Engineering, G. Pulla Reddy Engineering 
      College, Kurnool, Andhra Pradesh, India.
FAU - Vijayalata, Y
AU  - Vijayalata Y
AD  - Department of Computer Science and Engineering, Gokaraju Rangaraju Institute of 
      Engineering and Technology, Hyderabad, Telangana, India.
FAU - Gopalachari, M Venu
AU  - Gopalachari MV
AD  - Department of Information Technology, Chaitanya Bharthi Institute of Technology, 
      Hyderabad, Telangana, India.
FAU - Siddiquee, KazyNoor-E-Alam
AU  - Siddiquee KE
AUID- ORCID: 0000-0003-4985-6507
AD  - Department of Computer Science and Engineering, University of Science & 
      Technology, Chattogram, Bangladesh.
LA  - eng
PT  - Journal Article
DEP - 20220627
PL  - United States
TA  - Comput Intell Neurosci
JT  - Computational intelligence and neuroscience
JID - 101279357
SB  - IM
MH  - Algorithms
MH  - Benchmarking
MH  - *Brain Neoplasms/diagnostic imaging
MH  - Engineering
MH  - Humans
MH  - *Support Vector Machine
PMC - PMC9252655
COIS- The authors declare that they have no conflicts of interest to report regarding 
      the present study.
EDAT- 2022/07/08 06:00
MHDA- 2022/07/09 06:00
PMCR- 2022/06/27
CRDT- 2022/07/07 02:36
PHST- 2022/04/27 00:00 [received]
PHST- 2022/05/30 00:00 [revised]
PHST- 2022/06/03 00:00 [accepted]
PHST- 2022/07/07 02:36 [entrez]
PHST- 2022/07/08 06:00 [pubmed]
PHST- 2022/07/09 06:00 [medline]
PHST- 2022/06/27 00:00 [pmc-release]
AID - 10.1155/2022/9015778 [doi]
PST - epublish
SO  - Comput Intell Neurosci. 2022 Jun 27;2022:9015778. doi: 10.1155/2022/9015778. 
      eCollection 2022.