Enabling to obtain brain activation signs, electroencephalography is currently used in many applications as a medical diagnostic method. Brain-Computer Interface (BCI) applications are developed to facilitate the lives of individuals who have not lost their brain functions, yet have lost their motor and communication abilities. In this study, a BCI system is proposed to classify using Bi-directional Long Short Term Memory (Bi-LSTM) neural networks.In the designed system, spectral entropy method including instantaneous frequency change of signal is used as feature fusion. In the study, EEG data of 10 participants are collected with Emotiv EPOC+ device using 2x2 visual stimulus matrix prepared on Unity. Each symbol of the 2x2 matrix includes stimulus such as Doctor, Police, Fireman and Family.These stimuluses are demonstrated to participants with a fixed order. As data collection protocol, 200ms stimulus time and 300ms interstimulus interval are used. As the performance success of classification, the average accuracy rates are obtained to be 98.6% for training set and 96.9% for the test set. In addition, in classification of P300 EEG signals, the results obtained via Bi-LSTM are compared with the results obtained using 1 Dimensional Convolutional Neural Networks (1DCNN) and Support Vector Machines (SVM) classification methods. Moreover, in the study, information transfer rate (ITR) is provided as 40.39 at an acceptable level.
Bilgisayarlı Tomografi (BT) görüntülerinde her bir kesitte ortaya çıkan şekil, sınır ve yoğunluk gibi değişikliklerden dolayı karaciğerin bölütlenmesi zor bir süreç olarak durmaktadır. Diğer bölütleme yöntemleri ile karşılaştırıldığında, derin öğrenme modelleri ile daha başarılı bölütleme sonuçları genel fenomendir. Bu çalışmada, abdomen bölgesinden alınmış BT taramalarındaki kesitler üzerinde karaciğerin bilgisayar destekli otomatik bölütlenmesi için, Maskeli Bölgesel-Evrişimsel Sinir Ağları (Maskeli B-ESA) kullanılarak çoklu-GPU ile hızlandırılmış bir yöntem önerilmiştir. Bu çalışmaya özgü hazırlanan karaciğer BT görüntü veriseti üzerinde, hem tek hem de çift GPU donanımsal yapısı ile deneysel çalışmalar yürütülmüştür. Önerilen yöntem kullanılarak elde edilen sonuçlar ile uzman hekim tarafından bulunan bölütleme sonuçları Dice benzerlik katsayısı (DSC), Jaccard benzerlik katsayısı (JSC), volumetrik örtüşme hatası (VOE), ortalama simetrik yüzey mesafesi (ASD) ve oransal hacim farkı (RVD) ölçüm parametreleri ile karşılaştırılmıştır. Önerilen yaklaşım ile test görüntüleri üzerinde yürütülen deneysel çalışmalarda DSC, JSC, VOE, ASD ve RVD bölütleme başarım metrikleri, sırasıyla 97.32, 94.79, 5.21, 0.390, -1.008 olarak hesaplanmıştır. Bu sonuçlar ile bu çalışma kapsamında önerilen yöntemin, karaciğerin bölütlenmesi için hekimlerin karar verme süreçlerinde yardımcı bir araç olarak kullanılabileceği görülmüştür.
Breast cancer is the most common type of cancer in women today, and it ranks second after lung cancer with a very high mortality rate. If it is detected late, the treatment of breast cancer becomes very difficult. Although there are various methods for the detection of breast cancer, there is still a need for auxiliary diagnosis and treatment methods. In this study, a hybrid method is proposed to investigate the development of basal-like breast tumors and classify basal-like breast cancer types using histopathological images. In the study, firstly, appropriate features that support the accurate classification between tumor and non-tumor regions are extracted from histopathological images. Then the dataset is created by combining the obtained features. In the last stage of the study, the classification of images is carried out by using bag of words (BoW) and deep neural networks (DNN) techniques in a hybrid manner. Generally, immunohistochemical markers are used for this classification, but the performance of these markers remains at 60%. The performance of the classification accuracy of the proposed system is increased with the proposed hybrid classifier based on feature fusion. As a result of the study, 94.5% classification accuracy is achieved on the training set, while 80.8% classification accuracy is succeed on the test set. As a result, it is verified that successful results are achieved in the classification of basal-like breast cancer on histopathological images using the proposed hybrid method based on feature fusion.
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