Detection of uveal melanoma using fuzzy and neural networks classifiers

Santos, Daniel F.; Espitia, Helbert Eduardo

doi:10.12928/telkomnika.v18i4.14228

Cited by 9 publications

(6 citation statements)

References 16 publications

(19 reference 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%

Texture Identification of Cancer Cell Using Tamura’s Feature for Precise Treatment to Avoid Metastasis: A New Era of Artificial Intelligence in Healthcare Industry 5.0

Santra,

Dey,

Ghosh

et al. 2024

AIS

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Texture plays a significant role in image processing where the images are computed to detect mortal diseases like cancer. Artificial Intelligence (AI) finds various approaches in the level of industry expert 5.0 for the precise treatment of this disease which helps to motivate and inculcate patient’s mind from the darkness of it. Cancer changes the structure of the affected area (cell and/or tissue and/or organ) in an irregular manner so the texture or coarseness of ROI (Region of Interest) changes rapidly in the patient’s body. Tamura’s feature is playing to detect different assessment parameters like linelikeness, coarseness, direction of texture, body roughness, smoothness, irregularity, contrast by which the model can assess whether a ROI of input dataset carcinogenic or not. This communication has detected the precise condition of the image of the affected area which helps medical practioner for proper diagnose and treatment to avoid metastasis. Here we use python 3.7 in Google Colab platform to execute the model. As the model is based on deep learning methodologies so this cloud platform helps us to reduce the computation time for big dataset.

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Section: Abnormal Shape and Sizementioning

confidence: 99%

Texture Identification of Cancer Cell Using Tamura’s Feature for Precise Treatment to Avoid Metastasis: A New Era of Artificial Intelligence in Healthcare Industry 5.0

Santra,

Dey,

Ghosh

et al. 2024

AIS

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“…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%

Skin Cancer Detection Using Kernel Fuzzy C-Means and Improved Neural Network Optimization Algorithm

Jia

Zhao

Molk

2021

Computational Intelligence and Neuroscience

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Early diagnosis of malignant skin cancer from images is a significant part of the cancer treatment process. One of the principal purposes of this research is to propose a pipeline methodology for an optimum computer-aided diagnosis of skin cancers. The method contains four main stages. The first stage is to perform a preprocessing based on noise reduction and contrast enhancement. The second stage is to segment the region of interest (ROI). This study uses kernel fuzzy C-means for ROI segmentation. Then, some features from the ROI are extracted, and then, a feature selection is used for selecting the best ones. The selected features are then injected into a support vector machine (SVM) for final identification. One important part of the contribution in this study is to propose a developed version of a new metaheuristic, named neural network optimization algorithm, to optimize both parts of feature selection and SVM classifier. Comparison results of the method with 5 state-of-the-art methods showed the approach’s higher superiority toward the others.

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“…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%

Hepatitis diagnosis using optimized KD-Trees and Neural Networks

Santos-Bustos¹,

Espitia²

2020

International Journal of Engineering Research and Technology

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This article uses two computational techniques to detect Hepatitis, an inflammatory disease that affects the liver. An evaluation of techniques to detect it is presented, including KD-Trees and Neural Networks. The results show that KD-Trees system had a 59% of success while the neural network system had a 72% of success rate. Also, an analysis was carried out for each result employing receiver operating characteristic curves.

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Detection of uveal melanoma using fuzzy and neural networks classifiers

Cited by 9 publications

References 16 publications

Texture Identification of Cancer Cell Using Tamura’s Feature for Precise Treatment to Avoid Metastasis: A New Era of Artificial Intelligence in Healthcare Industry 5.0

Texture Identification of Cancer Cell Using Tamura’s Feature for Precise Treatment to Avoid Metastasis: A New Era of Artificial Intelligence in Healthcare Industry 5.0

Skin Cancer Detection Using Kernel Fuzzy C-Means and Improved Neural Network Optimization Algorithm

Hepatitis diagnosis using optimized KD-Trees and Neural Networks

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