2020
DOI: 10.12928/telkomnika.v18i4.14228
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Detection of uveal melanoma using fuzzy and neural networks classifiers

Abstract: The use of image processing is increasingly utilized for disease detection. In this article, an algorithm is proposed to detect uveal melanoma (UM) which is a type of intraocular cancer. The proposed method integrates algorithms related to iris segmentation and proposes a novel algorithm for the detection of UM from the approach of fuzzy logic and neural networks. The study case results show 76% correct classification in the fuzzy logic system and 96.04% for the artificial neural networks.

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Cited by 9 publications
(6 citation statements)
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“…Cancer cells often have irregular shapes and sizes compared to normal cells. They may be larger or smaller than surrounding healthy cells and may display an asymmetrical or distorted morphology [13][14].…”
Section: Abnormal Shape and Sizementioning
confidence: 99%
“…Cancer cells often have irregular shapes and sizes compared to normal cells. They may be larger or smaller than surrounding healthy cells and may display an asymmetrical or distorted morphology [13][14].…”
Section: Abnormal Shape and Sizementioning
confidence: 99%
“…e compared methods include Astorino's method [5] based on multiple instance learning (MIL), Hassan's method [6] based on a simple pipeline image preprocessing technique, Barros's method [7] based on hardware designing by the multilayer perceptron, Santos's method [8] based on neural networks and fuzzy logic, and Wang's method [9] based on deep convolution networks. e results are achieved from the mean value of both databases.…”
Section: Implementation Detailsmentioning
confidence: 99%
“…Santos and Espitia [8] presented an approach for the diagnosis of uveal melanoma (UM), which is a sort of intraocular cancer.…”
Section: Introductionmentioning
confidence: 99%
“…The help of computers as means to cooperate in the diagnosis of diseases has a wide variety of applications in health organizations. The early diagnosis of diseases helps to save lives, therefore, the development of tools that aid this end is of vital importance [1]. For this purpose, a set of classifiers was implemented to identify the presence of Hepatitis.…”
Section: Introductionmentioning
confidence: 99%