Mühendislik Fakültesi / Faculty of Engineering

Permanent URI for this collectionhttps://hdl.handle.net/11727/1401

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Author: search.filters.author.Agildere, Muhtesem

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    Automated Tuberculosis Detection Using Pre-Trained CNN and SVM
    (2021) Oltu, Burcu; Guney, Selda; Dengiz, Berna; Agildere, Muhtesem
    Tuberculosis (TB) is a dreadfully contagious and life-threatening disease if left untreated. Therefore, early and accurate diagnosis is critical for treatment. Today, invasive, expensive, or time-consuming tests are performed for diagnosis. Unfortunately, accurate TB diagnosis is still a major challenge. In the proposed study, a decision support system that can automatically separate normal and TB chest X-ray (CXR) images is presented for objective and accurate diagnosis. In the presented methodology, first various data augmentation methods were applied to the data set, then pre-trained networks (VGG16, MobileNet), were employed as feature extractors from augmented CXR's. Afterward, the extracted features for all images were fed into a support vector machine classifier. In training process, 5-fold cross-validation was applied. As a result of this classification, it was concluded that TB can be diagnosed with an accuracy of 96,6% and an area under the ROC curve (AUC) of 0,99.
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    Comparative Study for Tuberculosis Detection by Using Deep Learning
    (2021) Karaca, Busra Kubra; Guney, Selda; Dengiz, Berna; Agildere, Muhtesem
    Tuberculosis (TB) is an infectious disease which becomes a significant health problem worldwide. Many people have been affected by this disease owing to deficiency of treatment and late or inaccuracy of diagnosis. Therefore, accurate and early diagnosis is the very major solution to checking and preventing the disease. A chest x-ray is a main diagnostic tool used to diagnose tuberculosis. This diagnostic method is limited by the availability of radiologists and the experience and skills of radiologists in reading x-rays. To overcome such a challenge, a computer-aided diagnosis (CAD) system is supposed for the radiologist to interpret chest x-ray images easily. In this study, a CAD system based upon transfer learning is developed for TB detection using Montgomery Country chest x-ray images. We used the VGG16, VGG19, DenseNet121, MobileNet, and InceptionV3 pre-trained CNN models to extract features automatically and used the Support Vector Machine (SVM) classifier to the detection of tuberculosis. Furthermore, data augmentation techniques were applied to boost the performance results. The proposed method performed the highest accuracy of 98.9% and area under the curve (AUC) of 1.00, respectively, with the DenseNet121 on augmented images.
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    Monitoring nodule progression in chest X-ray images
    (2018) Sumer, Emre; Engin, Muharrem; Agildere, Muhtesem; Ogul, Hasan
    Lung nodules are frequently observed in cases of cancer. Nodules can be monitored with technologies such as computed tomography (CT) or magnetic resonance imaging (MRI). However. x-ray imaging is a low-cost method as well as its widespread usage. In this context, monitoring the nodules in short intervals by x-ray imaging gives benefits in many aspects. In this study, a three-stage novel approach is proposed to trace the nodule progressions from the lung x-ray images, automatically. In the first stage, x-ray images of a patient taken at different times must be registered to evaluate the nodule progression. To perform the registration, feature extraction and matching methods are employed, and then the homography matrix is calculated. In the second stage, according to previously known nodule positions, matched nodules are detected on registered images. Mismatched nodules in the first image are considered as lost, while the nodules only found in the second image are evaluated as newly appeared. In the last stage, nodules are considered as closed contours consisting of pixel set where closed contour area is calculated after nodule matching process. In this way, growth and shrink states are determined numerically. To test the proposed approach, a patient data set provided by Baskent University, Department of Radiology is used. The validation of the test results is performed by an expert radiologist According to the results obtained, the presented nodule progression trace system is found promising.