•Warthin-like papillary thyroid carcinoma: A rare tumor of the thyroid

Can, Nuray

doi:10.5798/diclemedj.0921.2011.04.0070

Cited by 2 publications

(1 citation statement)

References 3 publications

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“…The annual incidence of pleural mesothelioma was 22.4/1,000,000 in Anatolia [9]. Several studies were carried out about MPM epidemiology, clinics in our region [10][11][12][13]. But there isn't any study on MPM disease diagnosis using artificial immune systems (AIS) and artificial neural networks (ANN) with prognostic data.…”

Section: Introductionmentioning

confidence: 99%

Use of artificial intelligence techniques for diagnosis of malignant pleural mesothelioma

Er¹,

Tanrıkulu²,

Abakay³

2015

Dicle Med J

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Objective: Malignant pleural mesothelioma is a highly aggressive tumor of the serous membranes, which in humans results from exposure to asbestos and asbestiform fibers. The incidence of malignant mesothelioma is extremely high in some Turkish villages where there is a low-level environmental exposure to erionite, a fibrous zeolite. Therefore epidemiological studies are difficult to perform in Turkey. Methods: In this paper, a study on malignant pleural mesothelioma disease diagnosis was realized by using artificial immune system. Also, the artificial immune system result was compared with the result of the multi-layer neural network focusing on malignant pleural mesothelioma disease diagnosis and using same database. The malignant pleural mesothelioma disease dataset were prepared from a faculty of medicine's database using patient's hospital reports. Results: 97.74% accuracy performance is obtained by artificial immune system. The accuracy results of artificial immune system algorithm are much better than the accuracy results of multi-layer neural network algorithm. Conclusion: This system is capable of conducting the classification process with a good performance to help the expert while deciding the healthy and patient subjects. So, this structure can be helpful as learning based decision support system for contributing to the doctors in their diagnosis decisions.

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Section: Introductionmentioning

confidence: 99%