LEE- Bilgisayar Mühendisliği-Doktora

Bu koleksiyon için kalıcı URI

http://hdl.handle.net/11527/19208

Gözat

Konu "akciğer hastalığı" ile LEE- Bilgisayar Mühendisliği-Doktora'a göz atma
  • Öge
    Developing a novel artificial intelligence based method for diagnosing chronic obstructive pulmonary disease
    (Graduate School, 2023-11-06) Moran, İnanç ; Altılar, Deniz Turgay ; 504072504 ; Computer Engineering
    Today, research on machine learning and deep learning continues intensively due to their success in data classification and applications used in practice and their capacity to accurately reveal the information in the data. Since the beginning of the 21st century, especially deep learning, has produced very successful results by leaving traditional learning models behind and revolutionizing the latest technology. In this context, the detection of a fatal and global disease using deep learning has been researched in this thesis. The motivation of this research is to introduce the first research on automated Chronic Obstructive Pulmonary Disease (COPD) diagnosis using deep learning and the first annotated dataset in this field. The primary objective and contribution of this research is the development and design of an artificial intelligence system capable of diagnosing COPD utilizing only the heart signal (electrocardiogram, ECG) of the patient. In contrast to the traditional way of diagnosing COPD, which requires spirometer tests and a laborious workup in a hospital setting, the proposed system uses the classification capabilities of deep transfer learning and the patient's heart signal, which provides COPD signs in itself and can be received from any modern smart device. Since the disease progresses slowly and conceals itself until the final stage, hospital visits for diagnosis are uncommon. Hence, the medical goal of this research is to detect COPD using a simple heart signal before it becomes incurable. Deep transfer learning frameworks, which were previously trained on a general image data set, are transferred to carry out an automatic diagnosis of COPD by classifying patients' electrocardiogram signal equivalents, which are produced by signal-to-image transform techniques. Xception, VGG-19, InceptionResNetV2, DenseNet-121, and "trained-from-scratch" convolutional neural network architectures have been investigated for the detection of COPD, and it is demonstrated that they are able to obtain high performance rates in classifying nearly 33.000 instances using diverse training strategies. The highest classification rate was obtained by the Xception model at 99%. Although machine learning and deep learning generate accurate results, until a certain date, these techniques were subject to "black box" discourse. Recently, explainability has become a crucial issue in deep learning. Despite the exceptional performance of deep learning algorithms in various tasks, it is difficult to explain their inner workings and decision-making mechanisms in a way that is understandable. Explainable AI methods enable the accurate prediction of the outcomes of an AI model or the comprehension of the decision-making process. The LIME and SHAP methods, which are among the models that make it possible to interpret the results of deep learning and machine learning models, have been investigated for the purpose of interpreting the classifications made in the thesis. This research shows that the newly introduced COPD detection approach is effective, easily applicable, and eliminates the burden of considerable effort in a hospital. It could also be put into practice and serve as a diagnostic aid for chest disease experts by providing a deeper and faster interpretation of ECG signals. Using the knowledge gained while identifying COPD from ECG signals may aid in the early diagnosis of future diseases for which little data is currently available.