AIMC Topic: Algorithms

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Systematic review of approaches to detection and classification of skin cancer using artificial intelligence: Development and prospects.

Computers in biology and medicine
In recent years, there has been a significant improvement in the accuracy of the classification of pigmented skin lesions using artificial intelligence algorithms. Intelligent analysis and classification systems are significantly superior to visual d...

Machine Learning Algorithms for Processing and Classifying Unsegmented Phonocardiographic Signals: An Efficient Edge Computing Solution Suitable for Wearable Devices.

Sensors (Basel, Switzerland)
The phonocardiogram (PCG) can be used as an affordable way to monitor heart conditions. This study proposes the training and testing of several classifiers based on SVMs (support vector machines), k-NN (k-Nearest Neighbor), and NNs (neural networks) ...

Composite Graph Neural Networks for Molecular Property Prediction.

International journal of molecular sciences
Graph Neural Networks have proven to be very valuable models for the solution of a wide variety of problems on molecular graphs, as well as in many other research fields involving graph-structured data. Molecules are heterogeneous graphs composed of ...

A deep learning framework for predicting disease-gene associations with functional modules and graph augmentation.

BMC bioinformatics
BACKGROUND: The exploration of gene-disease associations is crucial for understanding the mechanisms underlying disease onset and progression, with significant implications for prevention and treatment strategies. Advances in high-throughput biotechn...

Development of a real-time cattle lameness detection system using a single side-view camera.

Scientific reports
Recent advancements in machine learning and deep learning have revolutionized various computer vision applications, including object detection, tracking, and classification. This research investigates the application of deep learning for cattle lamen...

GCTNet: a graph convolutional transformer network for major depressive disorder detection based on EEG signals.

Journal of neural engineering
Identifying major depressive disorder (MDD) using objective physiological signals has become a pressing challenge.Hence, this paper proposes a graph convolutional transformer network (GCTNet) for accurate and reliable MDD detection using electroencep...

Big data and deep learning for RNA biology.

Experimental & molecular medicine
The exponential growth of big data in RNA biology (RB) has led to the development of deep learning (DL) models that have driven crucial discoveries. As constantly evidenced by DL studies in other fields, the successful implementation of DL in RB depe...

Identification of diabetic retinopathy classification using machine learning algorithms on clinical data and optical coherence tomography angiography.

Eye (London, England)
BACKGROUND: To apply machine learning (ML) algorithms to perform multiclass diabetic retinopathy (DR) classification using both clinical data and optical coherence tomography angiography (OCTA).

Estimation of invasive coronary perfusion pressure using electrocardiogram and Photoplethysmography in a porcine model of cardiac arrest.

Computer methods and programs in biomedicine
BACKGROUND: Coronary perfusion pressure (CPP) indicates spontaneous return of circulation and is recommended for high-quality cardiopulmonary resuscitation (CPR). This study aimed to investigate a method for non-invasive estimation of CPP using elect...

Deep Learning Classification and Quantification of Pejorative and Nonpejorative Architectures in Resected Hepatocellular Carcinoma from Digital Histopathologic Images.

The American journal of pathology
Liver resection is one of the best treatments for small hepatocellular carcinoma (HCC), but post-resection recurrence is frequent. Biotherapies have emerged as an efficient adjuvant treatment, making the identification of patients at high risk of rec...