Browsing by Author "Oksuz C."

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A lightweight deep model for brain tumor segmentation
(2021-06-09) Oksuz C.; Urhan O.; Gullu M.K.
Brain tumors are one of the major causes of increasing deaths worldwide. It is important to correctly identify cancerous tissues by experts in order to make correct treatment planning and to increase patient survival rates. However, manually tracking and segmentation of cancerous tissues in many sections of volumetric MR data is an error-prone and time-consuming process. Developments in the field of deep learning in recent years allow the tasks performed by humans to be performed with higher accuracy and speeds through the developed automatic systems. In this study, a deep learning-based light-weighted model with 6.78M parameters is proposed for the classification of cancerous tissues in the brain. Cross-validation of the proposed method on a public data set results in 84.61%, 82.54%, and 87.15% Boundary F1, mean IoU, and mean accuracy, respectively, shows the robustness of the proposed model.
Brain Tumor Localization Using Yolo v2
(2020-10-05) Oksuz C.; M. Kemal Gullu
Deep Feature Extraction Based Fine-Tuning
(2020-10-05) Oksuz C.; Gullu M.K.
In this study, an optimal deep feature set is determined for the classification of brain tumor tissue types Glioma, Meningioma and Pituitary. It is a good alternative to use a pre-trained network that is trained with millions of data in areas such as medical imaging where the amount of available data is not sufficient to train a CNN. Another method is to train the last layers of the network known as fine-tuning by configuring the problem at hand. In this study, AlexNet is used. Then, an SVM model is trained using features extracted from pre-trained and fine-tuned networks. Experimental results show that the 3D feature vectors extracted from the fine-tuned network yield superior results in a different test set, compared to the feature vectors extracted directly from the pre-trained one and increase the generalization ability of the model.
Ensemble-LungMaskNet: Automated lung segmentation using ensembled deep encoders
(2021-08-25) Oksuz C.; Urhan O.; Gullu M.K.
Automated lung segmentation has importance because it gives clues about several diseases to the experts. It is the step that comes before further detailed analyses of the lungs. However, segmentation of the lungs is a challenging task since the opacities and consolidations are caused by various lung diseases. As a result, the clarity of the borders of the lungs may be lost which makes the segmentation task difficult. The presence of various medical equipment such as cables in the image is another factor that makes segmentation difficult. Therefore, it is a necessity to develop methods that can handle such situations. Learning the most useful patterns related to various diseases is possible with deep learning methods. Unlike conventional methods, learning the patterns improves the generalization ability of the models on unseen data. For this purpose, a deep segmentation framework including ensembles of pre-trained lightweight networks is proposed for lung region segmentation in this work. The experimental results achieved on two publicly available data sets demonstrate the effectiveness of the proposed framework.