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Analysing and Distinguishing Images of Failed Skin Cancer using Modern Swarm Intelligent Techniques(MSITs)
Author(s) -
Mohanad Aljanabi,
Jameel Kaduim Abed,
Mohammed Ali,
Jasim Mohmed Jasim Jasim,
Nadia Alanı
Publication year - 2020
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.179
H-Index - 26
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/745/1/012090
Subject(s) - thresholding , computer science , segmentation , artificial intelligence , skin cancer , pattern recognition (psychology) , image segmentation , image processing , filter (signal processing) , cancer , image (mathematics) , machine learning , computer vision , medicine
One of the damaging diseases among people in the world is skin cancer. Skin cancer leftovers an important scientific, clinical and public task. Swarm intelligence techniques (SITs) are new, improved and modern methods for optimization algorithms. Failure of detection in skin cancer images can be seen in SITs. This work proposes an efficient image and examines for some samples in this disease. The study presents a simple technique for a pre-processing and an automatic detection of SITs to make the needed analysis. This paper estimated all these various models using the PH 2 , Dermis, ISIC (2016, 2017, 2018) segmentation challenge dataset. The input images are improved for better processing than, the lesion sampling is segmented from the improved image by using Otsu thresholding and median filter operations. In the earlier studies, skin cancer is analyzed by means of several optimization algorithms. Now, the outcomes of the above algorithms were compared with the dice coefficient and it was demonstrated that the value of 97.35% which is nearer to manual segmentation. The accuracy the value of 98.58% when used for solving the same problem. To this end, a somewhat comprehensive analysis was showed to compare the effectiveness of many parameters’ combinations.

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