Classification of Hematoxylin and Eosin Images Using Local Binary Patterns and 1-D SIFT Algorithm†

Classification of fMRI Data by Using Clustering Moğultay, Hazal; Yarman Vural, Fatoş Tunay (2015-05-19) Recognition of the the cognitive states by using functional Magnetic Rezonans Imaging (fMRI) data is a challenging problem that has been a focus of scientific research for a long time. In this study the effectiveness of clustering and the ensemble learning techniques on fMRI dataset is investigated and different paramaters are compared. Moreover, the performance of these techniques are tested on both raw voxel intensity values and meshes formed by multiple voxels. Clusters are compared to the functional bra...
Classification of fNIRS Data Using Deep Learning for Bipolar Disorder Detection Evgin, Haluk Barkin; Babacan, Oguzhan; Ulusoy, İlkay; Hosgoren, Yasemin; Kusman, Adnan; Sayar, Damla; Baskak, Bora; Ozguven, Halise Devrimci (2019-01-01) With the use of ecologically validated tools more applicable measurements can be obtained, especially of individuals who have psychological disorders. Functional Near-Infrared Spectroscopy (fNIRS) is a neural imaging method that comes into prominence for imaging patients who have psychological disorders. It is a desired method because of its feasibility, high resolution in time and its partial resistance to head movements. Following the developments in the artificial intelligence, individuals' medical data ...
Classification of 4-class Motor Imagery EEG Data with Common Sparse Spectral Spatial Pattern Method Akinci, Berna; Gençer, Nevzat Güneri (2009-01-01) Brain Computer Interface aims to provide a communication system with external media via thougths. For this purpose, brain signals are acquired from the scalp by EEG device and processed for characterization. In this work, the classification of movement imagery EEG data has been studied for left hand, right hand, foot and tongue movement imagination cases. Common Spatial Patterns (CSP) method and temporal filters have been used in classification and Common Sparse Spectral Spatial Patterns (CSSSP) method has ...
Classification of lung nodules in CT images using convolutional neural networks Polat, Görkem; Serinağaoğlu Doğrusöz, Yeşim; Halıcı, Uğur; Department of Electrical and Electronics Engineering (2018) Recent studies have shown that lung cancer screening using annual low-dose computed tomography (CT) reduces lung cancer mortality by 20% compared to traditional chest radiography. Therefore, CT lung screening has started to be used widely all across the world. However, analyzing these images is a serious burden for radiologists. The number of slices in a CT scan can be up to 600. Therefore, computeraided-detection (CAD) systems are very important for faster and more accurate assessment of the data. In this ...
Classification of remotely sensed data by using 2D local discriminant bases Tekinay, Çağrı; Çetin, Yasemin; Department of Information Systems (2009) In this thesis, 2D Local Discriminant Bases (LDB) algorithm is used to 2D search structure to classify remotely sensed data. 2D Linear Discriminant Analysis (LDA) method is converted into an M-ary classifier by combining majority voting principle and linear distance parameters. The feature extraction algorithm extracts the relevant features by removing the irrelevant ones and/or combining the ones which do not represent supplemental information on their own. The algorithm is implemented on a remotely sensed...

Citation Formats

O. OGUZ, A. E. ÇETİN, and R. Atalay, “Classification of Hematoxylin and Eosin Images Using Local Binary Patterns and 1-D SIFT Algorithm†,” 2018, vol. 2, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/69559.