Orthodontically-induced external root resorption (OIERR) is among the most common risks in orthodontic treatment. Traditional OIERR diagnosis is limited by subjective judgement as well as cumbersome manual measurement. The research aims to develop an...
Deep convolutional neural networks (CNNs) have seen significant growth in medical image classification applications due to their ability to automate feature extraction, leverage hierarchical learning, and deliver high classification accuracy. However...
Existing deep learning methods for brain-computer interfaces (BCIs) based on steady-state visually evoked potential (SSVEP) face several challenges, such as overfitting when training data are insufficient, and the difficulty of effectively capturing ...
The detection of trace isoprene in breath provides a noninvasive method for lung cancer diagnosis. However, the presence of interfering components and the parts per billion (ppb) concentration levels of isoprene in breath complicate detection. In thi...
Oral cancer is a hazardous disease and a major cause of morbidity and mortality worldwide. The purpose of this study was to develop the deep convolutional neural networks (CNN)-based multiclass classification and object detection models for distingui...
Retinal fundus images provide valuable insights into the human eye's interior structure and crucial features, such as blood vessels, optic disk, macula, and fovea. However, accurate segmentation of retinal blood vessels can be challenging due to imba...
This paper presents a novel framework for accurate exercise posture recognition and health indicator prediction based on improved convolutional neural networks. We propose a multi-scale feature fusion architecture incorporating spatiotemporal attenti...
Melanoma is among the deadliest forms of malignant skin cancer, with the number of cases increasing dramatically worldwide. Its early and accurate diagnosis is crucial for effective treatment. However, automatic melanoma detection has several signifi...
Classifying bird species is essential for ecological study and biodiversity protection, currently, conventional approaches are frequently laborious and susceptible to mistakes. Convolutional Neural Networks (CNNs) provide a more reliable option for f...
Given artificial intelligence's transformative effects, studying safety is important to ensure it is implemented in a beneficial way. Convolutional neural networks are used in radiology research for prediction but can be corrupted through adversarial...
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