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Toplam kayıt 12, listelenen: 1-10
HIC-net: A deep convolutional neural network model for classification of histopathological breast images
(PERGAMON-ELSEVIER SCIENCE LTD, 2019)
In this study, a convolutional neural network (CNN) model is presented to automatically identify cancerous areas on whole-slide histopathological images (WSI). The proposed WSI classification network (HIC-net) architecture ...
Random fully connected layered 1D CNN for solving the Z-bus loss allocation problem
(Elsevier Sci Ltd, 2021)
Power loss allocation methods should be efficient enough to meet the needs of the customers on the bus and effectively calculate the losses from generators and consumers. In order to perform these tasks, a highly robust ...
Class-driven content-based medical image retrieval using hash codes of deep features
(Elsevier Sci Ltd, 2021)
Medical imaging provides the convenience of physicians to analyze the disease by providing visual data of the body parts required for clinical research and treatment. Today, increasing medical images following technological ...
Focal modulation network for lung segmentation in chest X-ray images
(Tubitak Scientific & Technological Research Council Turkey, 2023)
Segmentation of lung regions is of key importance for the automatic analysis of Chest X-Ray (CXR) images, which have a vital role in the detection of various pulmonary diseases. Precise identification of lung regions is ...
An adaptive deep learning framework to classify unknown composite power quality event using known single power quality events
(Pergamon-Elsevier Science Ltd, 2021)
Distributed generation (DG) sources are preferred to meet today's energy needs effectively. The addition of many different types of renewable energy sources to the grid causes various problems in signal quality. Detection ...
Focal Modulation Based End-to-End Multi-Label Classification for Chest X-ray Image Classification
(Ieee, 2023)
Chest X-ray imaging is of critical importance in order to effectively diagnose chest diseases, which are increasing today due to various environmental and hereditary factors. Although chest X-ray is the most commonly used ...
A novel classification framework using multiple bandwidth method with optimized CNN for brain-computer interfaces with EEG-fNIRS signals
(Springer London Ltd, 2021)
The most effective way to communicate between the brain and electronic devices in the outside world is the brain-computer interface (BCI) systems. BCI systems use signals of being through neural activity in the brain to ...
Residual CNN plus Bi-LSTM model to analyze GPR B scan images
(Elsevier, 2021)
In this study, the residual Convolutional Neural Network (CNN) with the Bidirectional Long Short Time Memory (Bi-LSTM) model has proposed for the analysis of Ground Penetrating Radar B scan (GPR B Scan) images. GPR ...
A novel hybrid deep learning approach including combination of 1D power signals and 2D signal images for power quality disturbance classification
(Pergamon-Elsevier Science Ltd, 2021)
As a result of the widespread use of power electronic equipment and the increase in consumption, the importance of effective energy policies and the smart grid begins to increase. Nonlinear loads and other loads in electric ...
An effective hashing method using W-Shaped contrastive loss for imbalanced datasets
(Pergamon-Elsevier Science Ltd, 2022)
The extraction of informative features from medical images and the retrieving of similar images from data repositories is vital for clinical decision support systems. Unlike general tasks such as medical image classification ...