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Artificial Intelligence Based Decision Support System for Early Diagnosis of Mesothelioma Disease

Yıl 2022, Cilt: 2 Sayı: 1, 8 - 13, 30.04.2022

Öz

Mesothelioma is a malignant tumor mostly seen in the membranes of heart and lungs. The exposure of these organs to substances such as asbestos and erionite causes mesothelioma disease. As a result of the deformation in these organs, shortness of breath, chest or back pain, cough and similar complaints occur. Because the symptoms of mesothelioma overlap with those of many other diseases, diagnosing the disease can be difficult and time-consuming. The goal of this study is to design an artificial intelligence-based early diagnosis system for mesothelioma disease. Two alternative neural network (NN) algorithms were utilized for this, and their results were analyzed. The performances of artificial neural network (ANN) and convolutional neural network (CNN) models were compared. F-measure rates for the designed ANN and CNN architectures were measured as 95% and 98%, respectively. The results showed that NN-based methods can be used in the early diagnosis of the disease. The software that will be built based on this model is expected to assist physicians in their decision-making processes.

Kaynakça

  • [1] Ergin, M. (2021, March, 29). 3 Soruda Mezotelyoma (Akciğer Zarı Kanseri) – Türk Göğüs Cerrahisi Derneği [Online]. Available: http://www.tgcd.org.tr/3-soruda-mezotelyoma-akciger-zari-kanseri/
  • [2] Tanrikulu, A. C., Abakay. A., & Kaplan M. A. (2010). A clinical, radiographic and laboratory evaluation of prognostic factors in 363 patients with malignant pleural mesothelioma. Respiration, 80, 480-487.
  • [3] Gülgösteren, M. (2021, March, 29). Mezotelyoma (Akciğer Zarı Kanseri) Nedir? Belirtileri ve Tedavi Yöntemleri. [Online]. Available: https://www.medicana.com.tr/
  • [4] Er, O., Tanrikulu, A. C., Abakay, A., & Temurtas. F. (2018). An approach based on probabilistic neural network for diagnosis of Mesothelioma’s disease. Computers and Electrical Engineering, 38(1), 75–81.
  • [5] Şentürk, K. Z., & Çekiç, N. A. (2020). Machine Learning Based Early Diagnosis System for Mesothelioma Disease, Düzce University Journal of Science & Technology, 8, 1604-1611.
  • [6] İlhan, H. O., & Çelik. E. (2016, October). The Mesothelioma Disease Diagnosis with Artificial Intelligence Methods. In Proceedings of the 10th International Conference on Application of Information and Communication Technologies (AICT), (pp. 1-5).
  • [7] Win, K. Y., Maneerat. N., Choomchuay, S., Sreng, S., & Hamamoto. K. (2018, November). Suitable Supervised Machine Learning Techniques For Malignant Mesothelioma Diagnosis. In Proceedings of the Biomedical Engineering International Conference (BMEiCON-2018), (pp. 225-239)
  • [8] Leong, M. (2020, October). A Comparative Study on Machine Learning Algorithms and A Hybrid Model of Genetic Algorithm and Neural Network for Mesothelioma Diagnosis. In Proceedings of the Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), (pp. 146-153)
  • [9] UCI Machine Learning Repository. (2021, March, 29). [Online]. Available: https://archive.ics.uci.edu/ml/datasets.php
  • [10] Ölmez, E., Akdoğan, V., & Er, O. (2020). Automatic Segmentation of Meniscus in MRI Using Regions with Convolutional Neural Network (R-CNN). Journal of Digital Imaging, 33(4), 916-929.
  • [11] Ölmez, E., Er, O., & Hızıroğlu, A. (2021). Deep Learning in BioMedical Applications: Detection of Lung Disease with Convolutional Neural Networks. In M. A. Jabbar, A. Abraham, O. Doğan, A. M. Madureira, & S. Tiwari (Eds.), Deep Learning in Biomedical and Health Informatics: Current Applications and Possibilities (pp.97-115), CRC Press.
Yıl 2022, Cilt: 2 Sayı: 1, 8 - 13, 30.04.2022

Öz

Kaynakça

  • [1] Ergin, M. (2021, March, 29). 3 Soruda Mezotelyoma (Akciğer Zarı Kanseri) – Türk Göğüs Cerrahisi Derneği [Online]. Available: http://www.tgcd.org.tr/3-soruda-mezotelyoma-akciger-zari-kanseri/
  • [2] Tanrikulu, A. C., Abakay. A., & Kaplan M. A. (2010). A clinical, radiographic and laboratory evaluation of prognostic factors in 363 patients with malignant pleural mesothelioma. Respiration, 80, 480-487.
  • [3] Gülgösteren, M. (2021, March, 29). Mezotelyoma (Akciğer Zarı Kanseri) Nedir? Belirtileri ve Tedavi Yöntemleri. [Online]. Available: https://www.medicana.com.tr/
  • [4] Er, O., Tanrikulu, A. C., Abakay, A., & Temurtas. F. (2018). An approach based on probabilistic neural network for diagnosis of Mesothelioma’s disease. Computers and Electrical Engineering, 38(1), 75–81.
  • [5] Şentürk, K. Z., & Çekiç, N. A. (2020). Machine Learning Based Early Diagnosis System for Mesothelioma Disease, Düzce University Journal of Science & Technology, 8, 1604-1611.
  • [6] İlhan, H. O., & Çelik. E. (2016, October). The Mesothelioma Disease Diagnosis with Artificial Intelligence Methods. In Proceedings of the 10th International Conference on Application of Information and Communication Technologies (AICT), (pp. 1-5).
  • [7] Win, K. Y., Maneerat. N., Choomchuay, S., Sreng, S., & Hamamoto. K. (2018, November). Suitable Supervised Machine Learning Techniques For Malignant Mesothelioma Diagnosis. In Proceedings of the Biomedical Engineering International Conference (BMEiCON-2018), (pp. 225-239)
  • [8] Leong, M. (2020, October). A Comparative Study on Machine Learning Algorithms and A Hybrid Model of Genetic Algorithm and Neural Network for Mesothelioma Diagnosis. In Proceedings of the Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), (pp. 146-153)
  • [9] UCI Machine Learning Repository. (2021, March, 29). [Online]. Available: https://archive.ics.uci.edu/ml/datasets.php
  • [10] Ölmez, E., Akdoğan, V., & Er, O. (2020). Automatic Segmentation of Meniscus in MRI Using Regions with Convolutional Neural Network (R-CNN). Journal of Digital Imaging, 33(4), 916-929.
  • [11] Ölmez, E., Er, O., & Hızıroğlu, A. (2021). Deep Learning in BioMedical Applications: Detection of Lung Disease with Convolutional Neural Networks. In M. A. Jabbar, A. Abraham, O. Doğan, A. M. Madureira, & S. Tiwari (Eds.), Deep Learning in Biomedical and Health Informatics: Current Applications and Possibilities (pp.97-115), CRC Press.
Toplam 11 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Klinik Tıp Bilimleri, Mühendislik
Bölüm Research Articles
Yazarlar

Emre Ölmez Bu kişi benim 0000-0003-1686-0251

Yayımlanma Tarihi 30 Nisan 2022
Yayımlandığı Sayı Yıl 2022 Cilt: 2 Sayı: 1

Kaynak Göster

APA Ölmez, E. (2022). Artificial Intelligence Based Decision Support System for Early Diagnosis of Mesothelioma Disease. Artificial Intelligence Theory and Applications, 2(1), 8-13.