Computational intelligence and neuroscience
Oct 6, 2022
Text classification plays an important role in many practical applications. In the real world, there are extremely small datasets. Most existing methods adopt pretrained neural network models to handle this kind of dataset. However, these methods are...
Computational intelligence and neuroscience
Oct 6, 2022
Interventional cardiology procedure is an important type of minimally invasive surgery that deals with the catheter-based treatment of cardiovascular diseases, such as coronary artery diseases, strokes, peripheral arterial diseases, and aortic diseas...
Computational intelligence and neuroscience
Oct 6, 2022
In recent days, research in human activity recognition (HAR) has played a significant role in healthcare systems. The accurate activity classification results from the HAR enhance the performance of the healthcare system with broad applications. HAR ...
Plasmodium falciparum is a parasitic protozoan that can cause malaria, which is a deadly disease. Therefore, the accurate identification of malaria parasite mitochondrial proteins is essential for understanding their functions and identifying novel d...
In response to the COVID-19 global pandemic, recent research has proposed creating deep learning based models that use chest radiographs (CXRs) in a variety of clinical tasks to help manage the crisis. However, the size of existing datasets of CXRs f...
Glaucoma is the second leading cause of blindness worldwide, and peripapillary atrophy (PPA) is a morphological symptom associated with it. Therefore, it is necessary to clinically detect PPA for glaucoma diagnosis. This study was aimed at developing...
OBJECTIVES: To compare image quality between a deep learning image reconstruction (DLIR) algorithm and conventional iterative reconstruction (IR) algorithms in dual-energy CT (DECT) and to assess the impact of these algorithms on radiomics robustness...
Unanticipated difficult laryngoscopy is associated with serious airway-related complications. We aimed to develop and test a convolutional neural network-based deep-learning model that uses lateral cervical spine radiographs to predict Cormack-Lehane...
OBJECTIVE: To evaluate the performance of a deep learning model for hippocampal sclerosis classification on the clinical dataset and suggest plausible visual interpretation for the model prediction.
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