AIMC Topic: Neural Networks, Computer

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BiLSTM- and CNN-Based m6A Modification Prediction Model for circRNAs.

Molecules (Basel, Switzerland)
m6A methylation, a ubiquitous modification on circRNAs, exerts a profound influence on RNA function, intracellular behavior, and diverse biological processes, including disease development. While prediction algorithms exist for mRNA m6A modifications...

HCA-DAN: hierarchical class-aware domain adaptive network for gastric tumor segmentation in 3D CT images.

Cancer imaging : the official publication of the International Cancer Imaging Society
BACKGROUND: Accurate segmentation of gastric tumors from CT scans provides useful image information for guiding the diagnosis and treatment of gastric cancer. However, automated gastric tumor segmentation from 3D CT images faces several challenges. T...

Development, comparison, and internal validation of prediction models to determine the visual prognosis of patients with open globe injuries using machine learning approaches.

BMC medical informatics and decision making
INTRODUCTION: Open globe injuries (OGI) represent a main preventable reason for blindness and visual impairment, particularly in developing countries. The goal of this study is evaluating key variables affecting the prognosis of open globe injuries a...

Refining neural network algorithms for accurate brain tumor classification in MRI imagery.

BMC medical imaging
Brain tumor diagnosis using MRI scans poses significant challenges due to the complex nature of tumor appearances and variations. Traditional methods often require extensive manual intervention and are prone to human error, leading to misdiagnosis an...

Enhanced multi-class pathology lesion detection in gastric neoplasms using deep learning-based approach and validation.

Scientific reports
This study developed a new convolutional neural network model to detect and classify gastric lesions as malignant, premalignant, and benign. We used 10,181 white-light endoscopy images from 2606 patients in an 8:1:1 ratio. Lesions were categorized as...

Stacked neural network for predicting polygenic risk score.

Scientific reports
In recent years, the utility of polygenic risk scores (PRS) in forecasting disease susceptibility from genome-wide association studies (GWAS) results has been widely recognised. Yet, these models face limitations due to overfitting and the potential ...

Svetlana a supervised segmentation classifier for Napari.

Scientific reports
We present Svetlana (SuperVised sEgmenTation cLAssifier for NapAri), an open-source Napari plugin dedicated to the manual or automatic classification of segmentation results. A few recent software tools have made it possible to automatically segment ...

COVID‑19 detection from chest X-ray images using transfer learning.

Scientific reports
COVID-19 is a kind of coronavirus that appeared in China in the Province of Wuhan in December 2019. The most significant influence of this virus is its very highly contagious characteristic which may lead to death. The standard diagnosis of COVID-19 ...

DLA-Net: dual lesion attention network for classification of pneumoconiosis using chest X-ray images.

Scientific reports
Accurate and early detection of pneumoconiosis using chest X-rays (CXR) is important for preventing the progression of this incurable disease. It is also a challenging task due to large variations in appearance, size and location of lesions in the lu...

Assessment of American Bullfrog (Lithobates catesbeianus) spreading in the Republic of Korea using rule learning of elementary cellular automata.

Scientific reports
The spread of American Bullfrog has a significant impact on the surrounding ecosystem. It is important to study the mechanisms of their spreading so that proper mitigation can be applied when needed. This study analyzes data from national surveys on ...