Scopus İndeksli Açık & Kapalı Erişimli Yayınlar
Permanent URI for this communityhttps://hdl.handle.net/11727/10752
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Item Reliability of Real-Time Sonoelastography in the Diagnosis of Supraspinatus Tendinopathy(2021) Aydin, Elcin; Soylev, Gozde Ozcan; Muratli, Sedit Kivanc; Limnili, Bora Bora; Boya, Hakan; Tekindal, Mustafa Agah; Agildere, Muhtesem; 0000-0001-8742-5543; 0000-0001-8742-5543; 31107427; AAI-8276-2021; AAJ-4917-2021The practicality of real-time sonoelastography in the diagnosis of tendinopathy is being discussed since the beginning of its use in musculoskeletal system. The aims of this study were to compare the elasticity of pathologic supraspinatus tendon with the uninvolved side by sonoelastography and to determine the relationship between the sonoelastographic findings and magnetic resonance imaging (MRI) grade of the tendinosis. Eighty-2 patients (50 males, 32 females, mean +/- SD age = 53.61 +/- 16.15 years, range = 20-84 years) with unilateral supraspinatus tendinosis were included in this study. Three grades of tendinosis were identified in MRI (grade 1: mild, grade 2: moderate, and grade 3: severe). The strain ratio (SR) of the tendinosis area to the healthy normal area of the same tendon tissue and SR of the tendinosis area to ipsilateral subcutaneous fat tissue were evaluated with sonoelastography. The SRs of the tendinosis areas were also compared with the control (contralateral) side tendon tissue and subcutaneous fat tissue of the same patients. Between-groups comparisons were also done according to the MRI grading. Statistical analysis was done using paired t test (P < 0.005 was considered statistically significant). There was a statistically significant difference in the comparison of the SRs of the tendinosis areas to subcutaneous fat tissues on ipsilateral shoulders (TA/SFT) and the healthy supraspinatus tendon area (TA/ST) of the same shoulder. There was also statistically significant difference when compared with the control side measurements (P < 0.01). In patients who have grade 1 and grade 3 tendinosis on MRI, there was statistically significant difference between elastrographic evaluation of affected and unaffected sides. Real-time sonoelastography is a reliable diagnostic method in patients with rotator cuff tendinosis and shall be kept in mind as a noninvasive, inexpensive, and practical diagnostic test in suitable cases.Item Automated Tuberculosis Detection Using Pre-Trained CNN and SVM(2021) Oltu, Burcu; Guney, Selda; Dengiz, Berna; Agildere, MuhtesemTuberculosis (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.Item Comparative Study for Tuberculosis Detection by Using Deep Learning(2021) Karaca, Busra Kubra; Guney, Selda; Dengiz, Berna; Agildere, MuhtesemTuberculosis (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.