AIMC Topic: Neural Networks, Computer

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Rice leaf diseases prediction using deep neural networks with transfer learning.

Environmental research
Rice (Oryza sativa) is a principal cereal crop in the world. It is consumed by greater than half of the world's population as a staple food for energy source. The yield production quantity and quality of the rice grain is affecting by abiotic and bio...

Semi-automated weak annotation for deep neural network skin thickness measurement.

Ultrasonic imaging
Correctly calculating skin stiffness with ultrasound shear wave elastography techniques requires an accurate measurement of skin thickness. We developed and compared two algorithms, a thresholding method and a deep learning method, to measure skin th...

Radiomics-based neural network predicts recurrence patterns in glioblastoma using dynamic susceptibility contrast-enhanced MRI.

Scientific reports
Glioblastoma remains the most devastating brain tumor despite optimal treatment, because of the high rate of recurrence. Distant recurrence has distinct genomic alterations compared to local recurrence, which requires different treatment planning bot...

Neural network aided approximation and parameter inference of non-Markovian models of gene expression.

Nature communications
Non-Markovian models of stochastic biochemical kinetics often incorporate explicit time delays to effectively model large numbers of intermediate biochemical processes. Analysis and simulation of these models, as well as the inference of their parame...

A Hybrid DCNN-SVM Model for Classifying Neonatal Sleep and Wake States Based on Facial Expressions in Video.

IEEE journal of biomedical and health informatics
Sleep is a natural phenomenon controlled by the central nervous system. The sleep-wake pattern, which functions as an essential indicator of neurophysiological organization in the neonatal period, has profound meaning in the prediction of cognitive d...

Preterm Newborn Presence Detection in Incubator and Open Bed Using Deep Transfer Learning.

IEEE journal of biomedical and health informatics
Video-based motion analysis recently appeared to be a promising approach in neonatal intensive care units for monitoring the state of preterm newborns since it is contact-less and noninvasive. However it is important to remove periods when the newbor...

Eliminating Indefiniteness of Clinical Spectrum for Better Screening COVID-19.

IEEE journal of biomedical and health informatics
The coronavirus disease 2019 (COVID-19) has swept all over the world. Due to the limited detection facilities, especially in developing countries, a large number of suspected cases can only receive common clinical diagnosis rather than more effective...

Generalized Fixation Invariant Nuclei Detection Through Domain Adaptation Based Deep Learning.

IEEE journal of biomedical and health informatics
Nucleus detection is a fundamental task in histological image analysis and an important tool for many follow up analyses. It is known that sample preparation and scanning procedure of histological slides introduce a great amount of variability to the...

Deep Learning for Diagnosis and Segmentation of Pneumothorax: The Results on the Kaggle Competition and Validation Against Radiologists.

IEEE journal of biomedical and health informatics
Pneumothorax is potentially a life-threatening disease that requires urgent diagnosis and treatment. The chest X-ray is the diagnostic modality of choice when pneumothorax is suspected. The computer-aided diagnosis of pneumothorax has received a dram...

A Multiprocessing Scheme for PET Image Pre-Screening, Noise Reduction, Segmentation and Lesion Partitioning.

IEEE journal of biomedical and health informatics
Accurate segmentation and partitioning of lesions in PET images provide computer-aided procedures and doctors with parameters for tumour diagnosis, staging and prognosis. Currently, PET segmentation and lesion partitioning are manually measured by ra...