TY - JOUR T1 - Artificial Intelligence Based Decision Support System for Early Diagnosis of Mesothelioma Disease AU - Ölmez, Emre PY - 2022 DA - April JF - Artificial Intelligence Theory and Applications JO - AITA PB - İzmir Bakırçay Üniversitesi WT - DergiPark SN - 2757-9778 SP - 8 EP - 13 VL - 2 IS - 1 LA - en AB - 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. KW - mesothelioma disease diagnosis KW - artificial intelligence KW - decision support system CR - [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/ CR - [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. CR - [3] Gülgösteren, M. (2021, March, 29). Mezotelyoma (Akciğer Zarı Kanseri) Nedir? Belirtileri ve Tedavi Yöntemleri. [Online]. Available: https://www.medicana.com.tr/ CR - [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. CR - [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. CR - [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). CR - [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) CR - [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) CR - [9] UCI Machine Learning Repository. (2021, March, 29). [Online]. Available: https://archive.ics.uci.edu/ml/datasets.php CR - [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. CR - [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. UR - https://dergipark.org.tr/en/pub/aita/issue//1136213 L1 - https://dergipark.org.tr/en/download/article-file/2509010 ER -