AIMC Topic: Neural Networks, Computer

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Abnormality classification and localization using dual-branch whole-region-based CNN model with histopathological images.

Computers in biology and medicine
The task of classification and localization with detecting abnormalities in medical images is considered very challenging. Computer-aided systems have been widely employed to address this issue, and the proliferation of deep learning network architec...

Boundary-aware glomerulus segmentation: Toward one-to-many stain generalization.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
The growing availability of scanned whole-slide images (WSIs) has allowed nephropathology to open new possibilities for medical decision-making over high-resolution images. Diagnosis of renal WSIs includes locating and identifying specific structures...

A Smart Visual Sensing Concept Involving Deep Learning for a Robust Optical Character Recognition under Hard Real-World Conditions.

Sensors (Basel, Switzerland)
In this study, we propose a new model for optical character recognition (OCR) based on both CNNs (convolutional neural networks) and RNNs (recurrent neural networks). The distortions affecting the document image can take different forms, such as blur...

Multi-Modal Brain Tumor Detection Using Deep Neural Network and Multiclass SVM.

Medicina (Kaunas, Lithuania)
Clinical diagnosis has become very significant in today's health system. The most serious disease and the leading cause of mortality globally is brain cancer which is a key research topic in the field of medical imaging. The examination and prognosi...

A neural network based approach to classify VLF signals as rock rupture precursors.

Scientific reports
The advent of novel technologies revealed that other geophysical signals than those directly related to fault motion could be used to probe the state of deformation of the Earth's crust. Electromagnetic signals belonging to this category have been in...

Acoustic scene classification based on three-dimensional multi-channel feature-correlated deep learning networks.

Scientific reports
As an effective approach to perceive environments, acoustic scene classification (ASC) has received considerable attention in the past few years. Generally, ASC is deemed a challenging task due to subtle differences between various classes of environ...

Deep learning for quality assessment of optical coherence tomography angiography images.

Scientific reports
Optical coherence tomography angiography (OCTA) is an emerging non-invasive technique for imaging the retinal vasculature. While there are many promising clinical applications for OCTA, determination of image quality remains a challenge. We developed...

Visualization deep learning model for automatic arrhythmias classification.

Physiological measurement
With the improvement of living standards, heart disease has become one of the common diseases that threaten human health. Electrocardiography (ECG) is an effective way of diagnosing cardiovascular diseases. With the rapid growth of ECG examinations a...

De novo spatiotemporal modelling of cell-type signatures in the developmental human heart using graph convolutional neural networks.

PLoS computational biology
With the emergence of high throughput single cell techniques, the understanding of the molecular and cellular diversity of mammalian organs have rapidly increased. In order to understand the spatial organization of this diversity, single cell data is...

A Method for Evaluating the Quality of Mathematics Education Based on Artificial Neural Network.

Computational and mathematical methods in medicine
Teaching quality evaluation (TQE) is an important link in the process of school teaching management. Evaluation indicators and teaching quality have a complicated and nonlinear connection that is influenced by several variables. Some of these drawbac...