X-ışınlı Göğüs İmgelerini Kullanarak Solunum Yolu Hastalıklarının Tespitinde Kuantum Transfer Öğrenme Modelinin Rolü

Year 2021, Volume: 9 Issue: 5, 1754 - 1765, 31.10.2021

Mesut Toğaçar

https://doi.org/10.29130/dubited.903358

Cited By: 1

Abstract

Solunum yolu hastalıkları çeşitli kanallar vasıtasıyla insanların solunum yollarına bulaşan; virüs ve bakteri gibi mikro organizmaların neden olduğu hastalıklardır. Bu canlılar vücudun bağışıklık sistemini zayıflatarak enfeksiyon oluşmasına yol açar ve bireyde kulak, burun, boğaz, solunum borusu ve akciğer gibi organlarda çoğalabilirler. Bunun sonucunda; zatürre, Ciddi Akut Solunum Sendromu (SARS), Orta Doğu Solunum Sendromu (MERS), Korona Virüs Hastalığı (COVID-19) gibi hastalıkların oluşmasına neden olabilmektedir ve erken müdahale alınmadığı takdirde hastaların ölümüne yol açabilmektedir. Bu çalışmada Kuantum modeli, derin öğrenme modeli ile yoğrularak farklı bir öğrenme yaklaşımı önerilmiştir. Bu model çeşitli kütüphane yazılımcıları tarafından verilen destekler ile gelişimini sürdürmektedir. Çalışmada kullanılan veri seti, solunum hastalıkları ve normal X-ışınları görüntülerinden oluşmaktadır. Deney analizinde, Kuantum Transfer Öğrenme (KTÖ) modeli kullanılarak veri setinin eğitimi gerçekleştirildi ve analiz sonuçlarından elde edilen doğruluk %92,50'ydi. Sonuç olarak, kuantum öğrenme modelinin derin öğrenme modelleri gibi umut verici sonuçlar verdiği bu çalışmada gözlemlendi.

Keywords

Derin öğrenme, Kuantum transfer öğrenme, Solunum hastalıkları, X-ışınları

The Role of Quantum Transfer Learning Model in the Detection of Respiratory Diseases Using X-ray Chest Images

Abstract

Respiratory diseases are transmitted to the respiratory tract of people through various channels; diseases caused by micro-organisms such as viruses and bacteria. These creatures weaken the body's immune system, leading to the formation of infection, and can reproduce in the individual in organs such as the ear, nose, throat, respiratory tract and lung. As a result; it can cause diseases such as "pneumonia, Serious Acute Respiratory Syndrome (SARS), Middle East Respiratory Syndrome (MERS), Corona Virus Disease (COVID-19)" and can lead to the death of patients if early intervention is not received. In this study, a different learning approach is proposed by combining the quantum model and the deep learning model. This model continues its development with the support provided by various library software developers. The dataset used in the study consists of respiratory diseases and normal X-ray images. In the experimental analysis, the dataset was trained using the Quantum Transfer Learning (QTL) model and the accuracy rate obtained from the analysis results was 92.50%. As a result, it was observed in this study that the Quantum approach gave promising results like deep learning models.

Keywords

Deep learning, Quantum transfer learning, Respiratory diseases, X-ray

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