Open AccessSkin Lesion Segmentation in Dermoscopy Imagery
Author(s) -
Sachin Garg,
Balkrishan Jindal
Publication year - 2022
Publication title -
the international arab journal of information technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.227
H-Index - 27
eISSN - 2309-4524
pISSN - 1683-3198
DOI - 10.34028/iajit/19/1/4
Subject(s) - sørensen–dice coefficient , computer science , segmentation , dice , skin lesion , artificial intelligence , skin cancer , sample (material) , pattern recognition (psychology) , image segmentation , statistics , mathematics , cancer , medicine , dermatology , chemistry , chromatography
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.