PMID- 36299601
OWN - NLM
STAT- MEDLINE
DCOM- 20221028
LR  - 20221028
IS  - 1942-0994 (Electronic)
IS  - 1942-0900 (Print)
IS  - 1942-0994 (Linking)
VI  - 2022
DP  - 2022
TI  - A Machine Learning Applied Diagnosis Method for Subcutaneous Cyst by 
      Ultrasonography.
PG  - 1526540
LID - 10.1155/2022/1526540 [doi]
LID - 1526540
AB  - For decades, ultrasound images have been widely used in the detection of various 
      diseases due to their high security and efficiency. However, reading ultrasound 
      images requires years of experience and training. In order to support the 
      diagnosis of clinicians and reduce the workload of doctors, many ultrasonic 
      computer aided diagnostic systems have been proposed. In recent years, the 
      success of deep learning in image classification and segmentation has made more 
      and more scholars realize the potential performance improvement brought by the 
      application of deep learning in ultrasonic computer-aided diagnosis systems. This 
      study is aimed at applying several machine learning algorithms and develop a 
      machine learning method to diagnose subcutaneous cyst. Clinical features are 
      extracted from datasets and images of ultrasonography of 132 patients from Hunan 
      Provincial People's Hospital in China. All datasets are separated into 70% 
      training and 30% testing. Four kinds of machine learning algorithms including 
      decision tree (DT), support vector machine (SVM), K-nearest neighbors (KNN), and 
      neural networks (NN) had been approached to determine the best performance. 
      Compared with all the results from each feature, SVM achieved the best 
      performance from 91.7% to 100%. Results show that SVM performed the highest 
      accuracy in the diagnosis of subcutaneous cyst by ultrasonography, which provide 
      a good reference in further application to clinical practice of ultrasonography 
      of subcutaneous cyst.
CI  - Copyright (c) 2022 Hao Feng et al.
FAU - Feng, Hao
AU  - Feng H
AD  - Department of Dermatology, Hunan Provincial People's Hospital (The First 
      Affiliated Hospital of Hunan Normal University), Changsha 410005, China.
FAU - Tang, Qian
AU  - Tang Q
AD  - Department of Dermatology, Hunan Provincial People's Hospital (The First 
      Affiliated Hospital of Hunan Normal University), Changsha 410005, China.
FAU - Yu, Zhengyu
AU  - Yu Z
AUID- ORCID: 0000-0002-8888-4570
AD  - Faculty of Engineering and IT, University of Technology, Sydney, Sydney, NSW 
      2007, Australia.
FAU - Tang, Hua
AU  - Tang H
AD  - Department of Dermatology, Hunan Provincial People's Hospital (The First 
      Affiliated Hospital of Hunan Normal University), Changsha 410005, China.
FAU - Yin, Ming
AU  - Yin M
AD  - Department of Dermatology, Hunan Provincial People's Hospital (The First 
      Affiliated Hospital of Hunan Normal University), Changsha 410005, China.
FAU - Wei, An
AU  - Wei A
AUID- ORCID: 0000-0002-8625-4209
AD  - Department of Ultrasound, Hunan Provincial People's Hospital (The First 
      Affiliated Hospital of Hunan Normal University), Changsha 410005, China.
LA  - eng
PT  - Journal Article
DEP - 20221017
PL  - United States
TA  - Oxid Med Cell Longev
JT  - Oxidative medicine and cellular longevity
JID - 101479826
SB  - IM
MH  - Humans
MH  - *Machine Learning
MH  - Support Vector Machine
MH  - Diagnosis, Computer-Assisted/methods
MH  - Ultrasonography
MH  - Algorithms
MH  - *Cysts/diagnostic imaging
PMC - PMC9592196
COIS- The authors declare no competing interests.
EDAT- 2022/10/28 06:00
MHDA- 2022/10/29 06:00
PMCR- 2022/10/17
CRDT- 2022/10/27 02:28
PHST- 2022/08/24 00:00 [received]
PHST- 2022/09/19 00:00 [revised]
PHST- 2022/09/28 00:00 [accepted]
PHST- 2022/10/27 02:28 [entrez]
PHST- 2022/10/28 06:00 [pubmed]
PHST- 2022/10/29 06:00 [medline]
PHST- 2022/10/17 00:00 [pmc-release]
AID - 10.1155/2022/1526540 [doi]
PST - epublish
SO  - Oxid Med Cell Longev. 2022 Oct 17;2022:1526540. doi: 10.1155/2022/1526540. 
      eCollection 2022.