AIMC Topic: Neural Networks, Computer

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Ensemble Learning, Deep Learning-Based and Molecular Descriptor-Based Quantitative Structure-Activity Relationships.

Molecules (Basel, Switzerland)
A deep learning-based quantitative structure-activity relationship analysis, namely the molecular image-based DeepSNAP-deep learning method, can successfully and automatically capture the spatial and temporal features in an image generated from a thr...

Automated detection of schizophrenia using deep learning: a review for the last decade.

Physiological measurement
Schizophrenia (SZ) is a devastating mental disorder that disrupts higher brain functions like thought, perception, etc., with a profound impact on the individual's life. Deep learning (DL) can detect SZ automatically by learning signal data character...

Transformer-based multi-task learning for classification and segmentation of gastrointestinal tract endoscopic images.

Computers in biology and medicine
Despite being widely utilized to help endoscopists identify gastrointestinal (GI) tract diseases using classification and segmentation, models based on convolutional neural network (CNN) have difficulties in distinguishing the similarities among some...

From Data to Diagnosis: How Machine Learning Is Changing Heart Health Monitoring.

International journal of environmental research and public health
The rapid advances in science and technology in the field of artificial neural networks have led to noticeable interest in the application of this technology in medicine. Given the need to develop medical sensors that monitor vital signs to meet both...

An automated segmentation of coronary artery calcification using deep learning in specific region limitation.

Medical & biological engineering & computing
Coronary artery calcification (CAC) is a frequent disease of the arteries that supply the surface of the heart muscle. Leaving a severe disease untreated can make it permanent. Computer tomography (CT), which is well known for its ability to quantify...

A New Deep-Learning Method for Human Activity Recognition.

Sensors (Basel, Switzerland)
Currently, three-dimensional convolutional neural networks (3DCNNs) are a popular approach in the field of human activity recognition. However, due to the variety of methods used for human activity recognition, we propose a new deep-learning model in...

Neural network based formation of cognitive maps of semantic spaces and the putative emergence of abstract concepts.

Scientific reports
How do we make sense of the input from our sensory organs, and put the perceived information into context of our past experiences? The hippocampal-entorhinal complex plays a major role in the organization of memory and thought. The formation of and n...

A novel deep learning model for breast lesion classification using ultrasound Images: A multicenter data evaluation.

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
PURPOSE: Breast cancer is one of the major reasons of death due to cancer in women. Early diagnosis is the most critical key for disease screening, control, and reducing mortality. A robust diagnosis relies on the correct classification of breast les...

NLS: An accurate and yet easy-to-interpret prediction method.

Neural networks : the official journal of the International Neural Network Society
Over the last years, the predictive power of supervised machine learning (ML) has undergone impressive advances, achieving the status of state of the art and super-human level in some applications. However, the employment rate of ML models in real-li...

Deep Learning Enhanced Electrochemiluminescence Microscopy.

Analytical chemistry
Limited by the efficiency of electrochemiluminescence, tens of seconds of exposure time are typically required to get a high-quality image. Image enhancement of short exposure time images to obtain a well-defined electrochemiluminescence image can me...