AIMC Topic: Deep Learning

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Entropy-driven deep learning framework for epilepsy detection using electro encephalogram signals.

Neuroscience May 5, 2025
Epilepsy is one of the most frequently occurring neurological disorders that require early and accurate detection. This paper introduces a novel approach for the automatic identification of epilepsy in EEG signals by incorporating advanced entropy-ba...
Humans Epilepsy Signal Processing, Computer-Assisted Entropy Deep Learning Electroencephalography Neural Networks, Computer
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A Novel 3D Approach with a CNN and Swin Transformer for Decoding EEG-Based Motor Imagery Classification.

Sensors (Basel, Switzerland) May 5, 2025
Motor imagery (MI) is a crucial research field within the brain-computer interface (BCI) domain. It enables patients with muscle or neural damage to control external devices and achieve movement functions by simply imagining bodily motions. Despite t...
Movement Imagination Brain-Computer Interfaces Signal-To-Noise Ratio Neural Networks, Computer Algorithms Electroencephalography Humans Signal Processing, Computer-Assisted Deep Learning
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Latent space autoencoder generative adversarial model for retinal image synthesis and vessel segmentation.

BMC medical imaging May 5, 2025
Diabetes is a widespread condition that can lead to serious vision problems over time. Timely identification and treatment of diabetic retinopathy (DR) depend on accurately segmenting retinal vessels, which can be achieved through the invasive techni...
Image Interpretation, Computer-Assisted Autoencoder Retinal Vessels Image Processing, Computer-Assisted Fundus Oculi Neural Networks, Computer Algorithms Deep Learning Humans Diabetic Retinopathy
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M-DeepAssembly: enhanced DeepAssembly based on multi-objective multi-domain protein conformation sampling.

BMC bioinformatics May 5, 2025
BACKGROUND: Association and cooperation among structural domains play an important role in protein function and drug design. Despite remarkable advancements in highly accurate single-domain protein structure prediction through the collaborative effor...
Algorithms Deep Learning Proteins Computational Biology Databases, Protein Protein Conformation Models, Molecular Protein Domains
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Optimizing non small cell lung cancer detection with convolutional neural networks and differential augmentation.

Scientific reports May 5, 2025
Lung cancer remains one of the leading causes of cancer-related deaths worldwide, with early detection being critical to improving patient outcomes. Recent advancements in deep learning have shown promise in enhancing diagnostic accuracy, particularl...
Deep Learning Humans Convolutional Neural Networks Algorithms Neural Networks, Computer Carcinoma, Non-Small-Cell Lung Lung Neoplasms
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Behavior recognition technology based on deep learning used in pediatric behavioral audiometry.

Scientific reports May 5, 2025
This study aims to explore the feasibility and accuracy of deep learning-based pediatric behavioral audiometry. The research provides a dedicated pediatric posture detection dataset, which contains a large number of video clips from children's behavi...
Posture Video Recording Hearing Tests Audiometry Hearing Deep Learning Child, Preschool Child Female Male Humans
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Advanced holographic convolutional dense networks and Tangent runner optimization for enhanced polycystic ovarian disease classification.

Scientific reports May 5, 2025
Polycystic Ovarian Disease (PCOD) is among the most prevalent endocrine disorders complicating the health of innumerable women worldwide due to lack of diagnosis and appropriate management. The diagnosis of PCOD, along with proper classification with...
Female Deep Learning Ultrasonography Humans Algorithms Holography Image Processing, Computer-Assisted Polycystic Ovary Syndrome
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An efficient patient's response predicting system using multi-scale dilated ensemble network framework with optimization strategy.

Scientific reports May 5, 2025
The forecasting of a patient's response to radiotherapy and the likelihood of experiencing harmful long-term health impacts would considerably enhance individual treatment plans. Due to the continuous exposure to radiation, cardiovascular disease and...
Humans Neural Networks, Computer Neoplasms Algorithms Deep Learning Prognosis
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TCN-QV: an attention-based deep learning method for long sequence time-series forecasting of gold prices.

PloS one May 5, 2025
Accurate prediction of gold prices is crucial for investment decision-making and national risk management. The time series data of gold prices exhibits random fluctuations, non-linear characteristics, and high volatility, making prediction extremely ...
Gold Forecasting Humans Commerce Deep Learning Neural Networks, Computer China
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Semisupervised adaptive learning models for IDH1 mutation status prediction.

PloS one May 5, 2025
The mutation status of isocitrate dehydrogenase1 (IDH1) in glioma is critical information for the diagnosis, treatment, and prognosis. Accurately determining such information from MRI data has emerged as a significant research challenge in recent yea...
Magnetic Resonance Imaging Mutation Isocitrate Dehydrogenase Algorithms Deep Learning Brain Neoplasms Glioma Humans
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