AIMC Topic: Neural Networks, Computer

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Establishing an AI-based diagnostic framework for pulmonary nodules in computed tomography.

BMC pulmonary medicine
BACKGROUND: Pulmonary nodules seen by computed tomography (CT) can be benign or malignant, and early detection is important for optimal management. The existing manual methods of identifying nodules have limitations, such as being time-consuming and ...

Deep learning algorithm for identifying osteopenia/osteoporosis using cervical radiography.

Scientific reports
Due to symptomatic gait imbalance and a high incidence of falls, patients with cervical disease-including degenerative cervical myelopathy-have a significantly increased risk of fragility fractures. To prevent such fractures in patients with cervical...

A deep learning approach for heart disease detection using a modified multiclass attention mechanism with BiLSTM.

Scientific reports
Heart disease remains the leading cause of death globally, mainly caused by delayed diagnosis and indeterminate categorization. Many of traditional ML/DL methods have limitations of misclassification, similar features, less training data, heavy compu...

Accurate and real-time brain tumour detection and classification using optimized YOLOv5 architecture.

Scientific reports
The brain tumours originate in the brain or its surrounding structures, such as the pituitary and pineal glands, and can be benign or malignant. While benign tumours may grow into neighbouring tissues, metastatic tumours occur when cancer from other ...

Neural network based AI model for lung health assessment.

Scientific reports
Treating pulmonary diseases is pivotal in healthcare since they are the third leading cause of mortality globally. To aid medical experts in diagnosis, various studies have been conducted using artificial intelligence (AI) compatible devices to analy...

EEG quantization and entropy of multi-step transition probabilities for driver drowsiness detection via LSTM.

Computers in biology and medicine
Detecting driver drowsiness through electroencephalogram (EEG) poses challenges due to the complexity and variability of brain activity across different subjects. This study proposes a feature extraction pipeline combined with a Long Short-Term Memor...

Deep homo-heterogeneous association mining with hybrid scholars and multidimensional mixed moment networks: Embedding-Driven prediction of microbe-drug interactions.

Computers in biology and medicine
Drug repurposing accelerates microbial therapy development by bypassing the costly and time-consuming traditional drug discovery process. However, existing computational methods for predicting drug-microbe associations (MDAs) struggle to capture comp...

Emotion recognition in EEG Signals: Deep and machine learning approaches, challenges, and future directions.

Computers in biology and medicine
A crucial part of brain-computer interfaces is the use of electroencephalogram (EEG) signals for human emotion identification, which analyzes patterns of brain activity to determine the emotional state. This field of study is becoming increasingly im...

ST-CFI: Swin Transformer with convolutional feature interactions for identifying plant diseases.

Scientific reports
The increasing global population, coupled with the diminishing availability of arable land, has rendered the challenge of ensuring food security more pronounced. The prompt and precise identification of plant diseases is essential for reducing crop l...

ModelS4Apnea: leveraging structured state space models for efficient sleep apnea detection from ECG signals.

Physiological measurement
. Sleep apnea is a common sleep disorder associated with severe health risks, necessitating accurate and efficient detection methods.. This study proposes ModelS4Apnea, a deep learning framework for sleep apnea detection from electrocardiogram (ECG) ...