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    A comparison of deep learning models for pneumonia detection from chest x-ray images
    (Gazi Universitesi, 2024) Kadiroğlu, Zehra; Deniz, Erkan; Şenyiğit, Abdurrahman
    Pneumonia is one of the acute lower respiratory tract diseases that can cause severe inflammation of the lung tissue. Although chest X-ray (CXR) is the most common clinical method for diagnosing pneumonia due to its low cost and ease of access, diagnosing pneumonia from CXR images is a difficult task even for specialist radiologists. It has been shown in the literature that deep learning-based image processing is effective in the automatic diagnosis of pneumonia. In conclusion, deep learning-based approaches were used in this study to classify pneumonia and healthy CXR images. These approaches are deep feature extraction, fine-tuning of pre-trained Convolutional Neural Networks (CNN), and end-to-end training of an enhanced ESA model. For deep feature extraction and transfer learning, 10 different pre-trained deep CNN models (AlexNet, ResNet50, DenseNet201, VGG16, VGG19, DarkNet53, ShuffleNet, Squeezenet, NASNetMobile and MobileNetV2) were used. Support Vector Machines (SVM), k Nearest Neighbor (kNN), Random Forest (RF) classifiers are used to classify deep features. The success of the fine-tuned AlexNet model produced an accuracy score of 98.50%, the highest of all results achieved. The end-to-end training of the developed ESA model yielded 96.75% results. The data set used in this study consists of Pneumonia and healthy CXR images obtained from Dicle University Medical Faculty Pulmonary Diseases and Tuberculosis clinic, intensive care unit and pulmonary outpatients’ clinic.
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    Öğe
    PneumoNet: Automated Detection of Pneumonia using Deep Neural Networks from Chest X-Ray Images
    (Fırat Üniversitesi, 2024) Kadiroğlu, Zehra; Deniz, Erkan; Kayaoğlu, Mazhar; Güldemir, Hanifi; Şenyiğit, Abdurrahman; Şengür, Abdülkadir
    Pneumonia is a dangerous disease that causes severe inflammation of the air sacs in the lungs. It is one of the infectious diseases with high morbidity and mortality in all age groups worldwide. Chest X-ray (CXR) is a diagnostic and imaging modality widely used in diagnosing pneumonia due to its low dose of ionizing radiation, low cost, and easy accessibility. Many deep learning methods have been proposed in various medical applications to assist clinicians in detecting and diagnosing pneumonia from CXR images. We have proposed a novel PneumoNet using a convolutional neural network (CNN) to detect pneumonia using CXR images accurately. Transformer-based deep learning methods, which have yielded high performance in natural language processing (NLP) problems, have recently attracted the attention of researchers. In this work, we have compared our results obtained using the CNN model with transformer-based architectures. These transformer architectures are vision transformer (ViT), gated multilayer perceptron (gMLP), MLP-mixer, and FNet. In this study, we have used the healthy and pneumonia CXR images from public and private databases to develop the model. Our developed PneumoNet model has yielded the highest accuracy of 96.50% and 94.29% for private and public databases, respectively, in detecting pneumonia accurately from healthy subjects.