Konu "Deep learning" için listeleme
Toplam kayıt 19, listelenen: 1-19
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Applications of Deep Learning Techniques to Wood Anomaly Detection
(2022)Wood products and structures have an important place in today's industry. They are widely used in many fields. However, there are various difficulties in production systems where wood raw material is under many processes. ... -
Automated Fracture Detection in the Ulna and Radius Using Deep Learning on Upper Extremity Radiographs
(2023)Objectives: This study aimed to detect single or multiple fractures in the ulna or radius using deep learning techniques fed on upper-extremity radiographs. Materials and methods: The data set used in the retrospective ... -
Classification of Canine Maturity and Bone Fracture Time Based on X-Ray Images of Long Bones
(2021)Veterinarians use X-rays for almost all examinations of clinical fractures to determine the appropriate treatment. Before treatment, vets need to know the date of the injury, type of the broken bone, and age of the dog. ... -
Classification of Human Movements by Using Kinect Sensor
(2023)In recent years, studies have been carried out to classify human movements in many areas such as health and safety. To classify human movements, image processing methods have also started to be used in recent years. With ... -
Computer-Aided Breast Cancer Diagnosis from Thermal Images Using Transfer Learning
(2020)Breast cancer is one of the prevalent types of cancer. Early diagnosis and treatment of breast cancer have vital importance for patients. Various imaging techniques are used in the detection of cancer. Thermal images are ... -
Deep neural network to differentiate brain activity between patients with euthymic bipolar disorders and healthy controls during verbal fluency performance: A multichannel near-infrared spectroscopy study
(2022)In this study, we aimed to differentiate between euthymic bipolar disorder (BD) patients and healthy controls (HC) based on frontal activity measured by fNIRS that were converted to spectrograms with Convolutional Neural ... -
Early and Late Level Fusion of Deep Convolutional Neural Networks for Visual Concept Recognition
(2016)Visual concept recognition is an active research field in the last decade. Related to this attention, deep learning architectures are showing great promise in various computer vision domains including image classification, ... -
Feature-level Fusion of Convolutional Neural Networks for Visual Object Classification
(2016)Deep learning architectures have shown great success in various computer vision applications. In this study, we investigate some of the very popular convolutional neural network (CNN) architectures, namely GoogleNet, ... -
Human Activity Recognition by Using Different Deep Learning Approaches for Wearable Sensors
(2021)With the spread of wearable sensors, the solutions to the task of activity recognition by using the data obtained from the sensors have become widespread. Recognition of activities owing to wearable sensors such as ... -
mirLSTM: A Deep Sequential Approach to MicroRNA Target Binding Site Prediction
(2019)MicroRNAs (miRNAs) are small and non-coding RNAs of similar to 21-23 base length, which play critical role in gene expression. They bind the target mRNAs in the post-transcriptional level and cause translational inhibition ... -
A new framework using deep auto-encoder and energy spectral density for medical waveform data classification and processing
(2019)This paper proposes a new framework for medical data processing which is essentially designed based on deep autoencoder and energy spectral density (ESD) concepts. The main novelty of this framework is to incorporate ESD ... -
A Novel Deep Learning Algorithm for the Automatic Detection of High-Grade Gliomas on T2-Weighted Magnetic Resonance I mages: A Preliminary Machine Learning Study
(2020)AIM: To propose a convolutional neural network (CNN) for the automatic detection of high-grade gliomas (HGGs) on T2-weighted magnetic resonance imaging (MRI) scans. MATERIAL and METHODS: A total of 3580 images obtained ... -
A real-time approach to recognition of Turkish sign language by using convolutional neural networks
(2021)Sign language is a form of visual communication used by people with hearing problems to express themselves. The main purpose of this study is to make life easier for these people. In this study, a data set was created using ... -
Recognizing visual places from landscapes with zero shot learning
(Başkent Üniversitesi Fen Bilimleri Enstitüsü, 2021)Image processing and deep learning methods are being developed day by day and the need of recognizing objects and extracting information from visual-based digital multimedia data like pictures and videos is increasing. ... -
A Systematic Review of Transfer Learning-Based Approaches for Diabetic Retinopathy Detection
(2023)Diabetic retinopathy, which is extreme visual blindness due to diabetes, has become an alarming issue worldwide. Early and accurate detection of DR is necessary to prevent the progression and reduce the risk of blindness. ... -
Utilizing Deep Convolutional Generative Adversarial Networks for Automatic Segmentation of Gliomas: An Artificial Intelligence Study
(2022)AIM: To describe a deep convolutional generative adversarial networks (DCGAN) model which learns normal brain MRI from normal subjects than finds distortions such as a glioma from a test subject while performing a segmentation ... -
Video concept classıfıcatıon and retrıeval
(Başkent Üniversitesi Fen Bilimleri Enstitüsü, 2016)Search and retrieval in video content is a trending topic in computer vision. Difficulties of this research topic is two folds; extracting semantic information from structure of video images is not a simple task and ... -
Virtual contrast enhancement for CT scans of abdomen and pelvis
(2022)Contrast agents are commonly used to highlight blood vessels, organs, and other structures in magnetic resonance imaging (MRI) and computed tomography (CT) scans. However, these agents may cause allergic reactions or ... -
Yolov3 and kalman filter based star detecting algorithm for cubesats’ star tracker sensors
(Başkent Üniversitesi Fen Bilimleri Enstitüsü, 2022)High accuracy of the attitude estimation in CubeSats is crucial for the reliability of the system and the complete fulfillment of CubeSat's missions. Star tracker cameras are reliable options for providing highly accurate ...