AIMC Topic: Neural Networks, Computer

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Deep learning approach for automated hMPV classification.

Scientific reports
Human metapneumovirus (hMPV) is a significant cause of respiratory illness, particularly in children, elderly individuals, and immunocompromised patients. Despite its clinical relevance, hMPV poses diagnostic challenges due to its symptom similarity ...

Advanced dynamic ensemble framework with explainability driven insights for precision brain tumor classification across datasets.

Scientific reports
Accurate detection of brain tumors remains a significant challenge due to the diversity of tumor types along with human interventions during diagnostic process. This study proposes a novel ensemble deep learning system for accurate brain tumor classi...

BCDCNN: breast cancer deep convolutional neural network for breast cancer detection using MRI images.

Scientific reports
Breast cancer (BC) is a kind of cancer that is created from the cells in breast tissue. This is a primary cancer that occurs in women. Earlier identification of BC is significant in the treatment process. To lessen unwanted biopsies, Magnetic Resonan...

LKDA-Net: Hierarchical transformer with large Kernel depthwise convolution attention for 3D medical image segmentation.

PloS one
Since Transformers have demonstrated excellent performance in the segmentation of two-dimensional medical images, recent works have also introduced them into 3D medical segmentation tasks. For example, hierarchical transformers like Swin UNETR have r...

LGD_Net: Capsule network with extreme learning machine for classification of lung diseases using CT scans.

PloS one
Lung diseases (LGDs) are related to an extensive range of lung disorders, including pneumonia (PNEUM), lung cancer (LC), tuberculosis (TB), and COVID-19 etc. The diagnosis of LGDs is performed by using different medical imaging such as X-rays, CT sca...

Gastrointestinal bleeding detection on digital subtraction angiography using convolutional neural networks with and without temporal information.

Diagnostic and interventional radiology (Ankara, Turkey)
PURPOSE: Digital subtraction angiography (DSA) offers a real-time approach to locating lower gastrointestinal (GI) bleeding. However, many sources of bleeding are not easily visible on angiograms. This investigation aims to develop a machine learning...

Exploring the clinical value of concept-based AI explanations in gastrointestinal disease detection.

Scientific reports
Complex artificial intelligence models, like deep neural networks, have shown exceptional capabilities to detect early-stage polyps and tumors in the gastrointestinal tract. These technologies are already beginning to assist gastroenterologists in th...

Development of several machine learning based models for determination of small molecule pharmaceutical solubility in binary solvents at different temperatures.

Scientific reports
Analysis of small-molecule drug solubility in binary solvents at different temperatures was carried out via several machine learning models and integration of models to optimize. We investigated the solubility of rivaroxaban in both dichloromethane a...

A novel approach for joint indoor localization and activity recognition using a hybrid CNN-GRU and MRF framework.

PloS one
This work proposes a new hybrid model for joint indoor localization and activity recognition by combining a Convolutional Neural Network-Gated Recurrent Unit (CNN-GRU) model with a Markov Random Field (MRF) for better classification. The CNN-GRU succ...

GNN-RMNet: Leveraging graph neural networks and GPS analytics for driver behavior and route optimization in logistics.

PloS one
Logistics networks are becoming increasingly complex and rely more heavily on real-time vehicle data, necessitating intelligent systems to monitor driver behavior and identify route anomalies. Traditional techniques struggle to capture the dynamic sp...