Mühendislik ve Doğa Bilimleri Fakültesi Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.13091/1624

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Citation - WoS: 1
Citation - Scopus: 1
Classification of Mammography Images by Transfer Learning
(IEEE, 2020) Solak, Ahmet; Ceylan, Rahime
Breast cancer is the most common cancer type in women worldwide. Diagnosis and early detection of cancer by mammography images are of great importance in cancer treatment. The use of deep learning in Computer Assisted Diagnostic systems has gained a great momentum especially since 2012. In this study, benign and malignant mass images were reproduced with data augmentation and the data sets obtained were classified with deep learning networks. In this study, a scratch Convolutional Neural Network (CNN) architecture was created and transfer learning was realized with different network models which trained on IMAGENET images. In the transfer learning section, separate training results were obtained by performing feature extraction and fine tuning of network parameters. As a result of the study, the best results were obtained with MobileNet, NASNetLarge and InceptionResNetV2 models which are used in transfer learning models.
Classification of Medical Thermograms Using Transfer Learning
(IEEE, 2020) Örnek, Ahmet Haydar; Ceylan, Murat
Thermal imaging has been used for decades to monitor the health status of neonates as an non-invasive and non-ionizing imaging technique. Applications such as thermal asymmetry and disease analysis can be performed by applying deep learning methods to thermal imaging technique. However, thousands of different images are needed to perform analyzes with deep learning methods. It takes many years to create data sets with thousands of different images due to feeding time, medication time and instant baby care in the neonatal intensive care unit. In this study, a unhealthy-healthy classification was performed using thermal images obtained from the Selcuk University, Faculty of Medicine, Neonatal Intensive Care Unit for one year. Transfer learning method has been used to overcome the lack of data problem. When VGG16 model was used for transfer learning, the results were obtained as 100% sensitivity and 94.73% specificity. This result shows that thermal imaging and transfer learning method can be used in early diagnosis of diseases.
Citation - Scopus: 1
Explainable Features in Classification of Neonatal Thermograms
(IEEE, 2020) Örnek, Ahmet Haydar; Ceylan, Murat
Although deep learning models perform high performance classifications (+90% accuracy), there is very limited research on the explanability of models. However, explaining why a decision is made in computer-assisted diagnoses and determining why untrained deep learning models cannot be trained is crucial for medical professionals to evaluate the decision. In this study, 190 thermal images of 38 different neonates who were hospitalized in the Neonatal Intensive Care Unit of the Faculty of Medicine, Selcuk University were trained to perform an ESA model unhealthy-healthy classification and visualization of the intermediate layer outputs. The train-validation-test accuracy of the model was 9738%, 3736% and 94.73%, respectively. By visualizing the intermediate layer outputs, it has been shown that ESA filters learn the characteristics of the baby (edge, tissue, body, temperature) rather than background (incubator, measurement cables) when performing unhealthy-healthy classification.
Citation - WoS: 1
Citation - Scopus: 8
Pancreas Segmentation in Abdominal Ct Images With U-Net Model
(IEEE, 2020) Kurnaz, Ender; Ceylan, Rahime
Pancreas is one of the most challenging organs in segmentation due to its different shape, position and size in each human being. With the development of machine learning, various deep learning methods are applied to segment the pancreas among organs in the abdominal region. In this study, pancreas segmentation is performed using the U-Net model, which is one of the convolutional neural networks (CNN) models. The results of pancreas segmentation performed on the Pancreas CT data set obtained from The Cancer Imaging Archive (TCIA) database containing computed tomography images of 82 patients are presented in detail. According to the results, Dice similarity coefficient and Jaccard similarity coefficient are found to be 0.78 and 0.66, respectively.

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Browsing Mühendislik ve Doğa Bilimleri Fakültesi Koleksiyonu by Journal "2020 28TH SIGNAL PROCESSING AND COMMUNICATIONS APPLICATIONS CONFERENCE (SIU)"