AIMC Topic: Neural Networks, Computer

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Crack Length Measurement Using Convolutional Neural Networks and Image Processing.

Sensors (Basel, Switzerland)
Fatigue failure is a significant problem in the structural safety of engineering structures. Human inspection is the most widely used approach for fatigue failure detection, which is time consuming and subjective. Traditional vision-based methods are...

Towards Interpretable Deep Learning: A Feature Selection Framework for Prognostics and Health Management Using Deep Neural Networks.

Sensors (Basel, Switzerland)
In the last five years, the inclusion of Deep Learning algorithms in prognostics and health management (PHM) has led to a performance increase in diagnostics, prognostics, and anomaly detection. However, the lack of interpretability of these models r...

AI outperformed every dermatologist in dermoscopic melanoma diagnosis, using an optimized deep-CNN architecture with custom mini-batch logic and loss function.

Scientific reports
Melanoma, one of the most dangerous types of skin cancer, results in a very high mortality rate. Early detection and resection are two key points for a successful cure. Recent researches have used artificial intelligence to classify melanoma and nevu...

Quantitative analysis of metastatic breast cancer in mice using deep learning on cryo-image data.

Scientific reports
Cryo-imaging sections and images a whole mouse and provides ~ 120-GBytes of microscopic 3D color anatomy and fluorescence images, making fully manual analysis of metastases an onerous task. A convolutional neural network (CNN)-based metastases segmen...

Efficacy of a comprehensive binary classification model using a deep convolutional neural network for wireless capsule endoscopy.

Scientific reports
The manual reading of capsule endoscopy (CE) videos in small bowel disease diagnosis is time-intensive. Algorithms introduced to automate this process are premature for real clinical applications, and multi-diagnosis using these methods has not been ...

DEEP picker is a deep neural network for accurate deconvolution of complex two-dimensional NMR spectra.

Nature communications
The analysis of nuclear magnetic resonance (NMR) spectra for the comprehensive and unambiguous identification and characterization of peaks is a difficult, but critically important step in all NMR analyses of complex biological molecular systems. Her...

Epistatic Net allows the sparse spectral regularization of deep neural networks for inferring fitness functions.

Nature communications
Despite recent advances in high-throughput combinatorial mutagenesis assays, the number of labeled sequences available to predict molecular functions has remained small for the vastness of the sequence space combined with the ruggedness of many fitne...

EAD-Net: A Novel Lesion Segmentation Method in Diabetic Retinopathy Using Neural Networks.

Disease markers
Diabetic retinopathy (DR) is a common chronic fundus disease, which has four different kinds of microvessel structure and microvascular lesions: microaneurysms (MAs), hemorrhages (HEs), hard exudates, and soft exudates. Accurate detection and countin...

Difference Analysis of Regional Economic Development Based on the SOM Neural Network with the Hybrid Genetic Algorithm.

Computational intelligence and neuroscience
Since the reform and opening up, China's regional economy has developed rapidly. However, due to different starting points of economic development caused by the traditional distribution of productive forces and the differences in regions, resources, ...

Automatic segmentation of gadolinium-enhancing lesions in multiple sclerosis using deep learning from clinical MRI.

PloS one
Gadolinium-enhancing lesions reflect active disease and are critical for in-patient monitoring in multiple sclerosis (MS). In this work, we have developed the first fully automated method to segment and count the gadolinium-enhancing lesions from rou...