Construction of adaptive pulse coupled neural network for abnormality detection in medical images. Issue 5 (28th May 2018)
- Record Type:
- Journal Article
- Title:
- Construction of adaptive pulse coupled neural network for abnormality detection in medical images. Issue 5 (28th May 2018)
- Main Title:
- Construction of adaptive pulse coupled neural network for abnormality detection in medical images
- Authors:
- Upadhyay, Pawan Kumar
Chandra, Satish - Abstract:
- ABSTRACT: In this article, we propose a customized pulse coupled neural network for image segmentation to detect the different classes of skin lesions by minimizing the number of pixels as image harmonics. In addition, we have used a distribution similar to primary visual cortex for internal activation function. This helps in transforming the visual behavior of the cortex. The developed neural synchrony of adaptive pulse coupled neural network helps in classifying the lesion patterns in dermoscopy images. We evaluate our proposed approach on 240 gold standard dermofit images of lesions. Our results have shown significant improvement in the accuracy and efficiency when compared with existing methods.
- Is Part Of:
- Applied artificial intelligence. Volume 32:Issue 5(2018)
- Journal:
- Applied artificial intelligence
- Issue:
- Volume 32:Issue 5(2018)
- Issue Display:
- Volume 32, Issue 5 (2018)
- Year:
- 2018
- Volume:
- 32
- Issue:
- 5
- Issue Sort Value:
- 2018-0032-0005-0000
- Page Start:
- 477
- Page End:
- 495
- Publication Date:
- 2018-05-28
- Subjects:
- Artificial intelligence -- Periodicals
006.3 - Journal URLs:
- http://www.tandfonline.com/toc/uaai20/current ↗
http://www.tandfonline.com/ ↗ - DOI:
- 10.1080/08839514.2018.1481818 ↗
- Languages:
- English
- ISSNs:
- 0883-9514
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 1571.650000
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 7278.xml