AIMC Topic: Neural Networks, Computer

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Tongue shape classification based on IF-RCNet.

Scientific reports
The classification of tongue shapes is essential for objective tongue diagnoses. However, the accuracy of classification is influenced by numerous factors. First, considerable differences exist between individuals with the same tongue shape. Second, ...

Pre-trained convolutional neural networks identify Parkinson's disease from spectrogram images of voice samples.

Scientific reports
Machine learning approaches including deep learning models have shown promising performance in the automatic detection of Parkinson's disease. These approaches rely on different types of data with voice recordings being the most used due to the conve...

A deep ensemble learning approach for squamous cell classification in cervical cancer.

Scientific reports
Cervical cancer, arising from the cells of the cervix, the lower segment of the uterus connected to the vagina-poses a significant health threat. The microscopic examination of cervical cells using Pap smear techniques plays a crucial role in identif...

Loss formulations for assumption-free neural inference of SDE coefficient functions.

NPJ systems biology and applications
Stochastic differential equations (SDEs) are one of the most commonly studied probabilistic dynamical systems, and widely used to model complex biological processes. Building upon the previously introduced idea of performing inference of dynamical sy...

Skin cancer detection using dermoscopic images with convolutional neural network.

Scientific reports
Skin malignant melanoma is a high-risk tumor with low incidence but high mortality rates. Early detection and treatment are crucial for a cure. Machine learning studies have focused on classifying melanoma tumors, but these methods are cumbersome and...

Intelligent biofilm detection with ensemble of deep learning networks.

Medicina oral, patologia oral y cirugia bucal
BACKGROUND: Dental biofilm is traditionally identified visually, which can be challenging and time-consuming due to its color similarity with the tooth. The aim of this study was to evaluate the performance of U-Net neural networks for the automatic ...

Deep learning for named entity recognition in Turkish radiology reports.

Diagnostic and interventional radiology (Ankara, Turkey)
PURPOSE: The primary objective of this research is to enhance the accuracy and efficiency of information extraction from radiology reports. In addressing this objective, the study aims to develop and evaluate a deep learning framework for named entit...

MultiKD-DTA: Enhancing Drug-Target Affinity Prediction Through Multiscale Feature Extraction.

Interdisciplinary sciences, computational life sciences
The discovery and development of novel pharmaceutical agents is characterized by high costs, lengthy timelines, and significant safety concerns. Traditional drug discovery involves pharmacologists manually screening drug molecules against protein tar...

Deep learning-based weed detection for precision herbicide application in turf.

Pest management science
BACKGROUND: Precision weed mapping in turf according to its susceptibility to selective herbicides allows the smart sprayer to spot-spray the most pertinent herbicides onto the susceptible weeds. The objective of this study was to evaluate the feasib...

A novel approach for estimating postmortem intervals under varying temperature conditions using pathology images and artificial intelligence models.

International journal of legal medicine
Estimating the postmortem interval (PMI) is a critical yet complex task in forensic investigations, with accurate and timely determination playing a key role in case resolution and legal outcomes. Traditional methods often suffer from environmental v...