Skin lesion segmentation in dermoscopy imagery
Joint Authors
Garg, Shelly
Jindal, Balkrishan
Source
The International Arab Journal of Information Technology
Issue
Vol. 19, Issue 1 (31 Jan. 2022), pp.29-37, 9 p.
Publisher
Zarqa University Deanship of Scientific Research
Publication Date
2022-01-31
Country of Publication
Jordan
No. of Pages
9
Main Subjects
Information Technology and Computer Science
Abstract EN
The main purpose of this study is to find an optimum method for segmentation of skin lesion images.
In the present world, Skin cancer has proved to be the most deadly disease.
The present research paper has developed a model which encompasses two gradations, the first being pre-processing for the reduction of unwanted artefacts like hair, illumination or many other by enhanced technique using threshold and morphological operations to attain higher accuracy and the second being segmentation by using k-mean with optimized Firefly Algorithm (FFA) technique.
The online image database from the International Skin Imaging Collaboration (ISIC) archive dataset and dermatology service of Hospital Pedro Hispano (PH2) dataset has been used for input sample images.
The parameters on which the proposed method is measured are sensitivity, specificity, dice coefficient, jacquard index, execution time, accuracy, error rate.
From the results, authors have observed proposed model gives the average accuracy value of huge number of cancer images using ISIC dataset is 98.9% and using PH2 dataset is 99.1% with minimize average less error rate.
It also estimates the dice coefficient value 0.993 using ISIC and 0.998 using PH2 datasets.
However, the results for the rest of the parameters remain quite the same.
Therefore the outcome of this model is highly reassuring.
American Psychological Association (APA)
Garg, Shelly& Jindal, Balkrishan. 2022. Skin lesion segmentation in dermoscopy imagery. The International Arab Journal of Information Technology،Vol. 19, no. 1, pp.29-37.
https://search.emarefa.net/detail/BIM-1437412
Modern Language Association (MLA)
Garg, Shelly& Jindal, Balkrishan. Skin lesion segmentation in dermoscopy imagery. The International Arab Journal of Information Technology Vol. 19, no. 1 (Jan. 2022), pp.29-37.
https://search.emarefa.net/detail/BIM-1437412
American Medical Association (AMA)
Garg, Shelly& Jindal, Balkrishan. Skin lesion segmentation in dermoscopy imagery. The International Arab Journal of Information Technology. 2022. Vol. 19, no. 1, pp.29-37.
https://search.emarefa.net/detail/BIM-1437412
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references : p. 34-36
Record ID
BIM-1437412