AIMC Topic: Algorithms

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Automatic detection of 39 fundus diseases and conditions in retinal photographs using deep neural networks.

Nature communications
Retinal fundus diseases can lead to irreversible visual impairment without timely diagnoses and appropriate treatments. Single disease-based deep learning algorithms had been developed for the detection of diabetic retinopathy, age-related macular de...

Business English Translation Model Based on BP Neural Network Optimized by Genetic Algorithm.

Computational intelligence and neuroscience
In recent years, most of the communication places are using business English manual simultaneous interpretation or electronic equipment translation. In the context of diverse cultures, the way English is used and its grammar vary from country to coun...

DeepFoveaNet: Deep Fovea Eagle-Eye Bioinspired Model to Detect Moving Objects.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Birds of prey especially eagles and hawks have a visual acuity two to five times better than humans. Among the peculiar characteristics of their biological vision are that they have two types of foveae; one shallow fovea used in their binocular visio...

WaveCNet: Wavelet Integrated CNNs to Suppress Aliasing Effect for Noise-Robust Image Classification.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Though widely used in image classification, convolutional neural networks (CNNs) are prone to noise interruptions, i.e. the CNN output can be drastically changed by small image noise. To improve the noise robustness, we try to integrate CNNs with wav...

Robust Phase Unwrapping via Deep Image Prior for Quantitative Phase Imaging.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Quantitative phase imaging (QPI) is an emerging label-free technique that produces images containing morphological and dynamical information without contrast agents. Unfortunately, the phase is wrapped in most imaging system. Phase unwrapping is the ...

Diagnostic performance and image quality of deep learning image reconstruction (DLIR) on unenhanced low-dose abdominal CT for urolithiasis.

Acta radiologica (Stockholm, Sweden : 1987)
BACKGROUND: Patients with urolithiasis undergo radiation overexposure from computed tomography (CT) scans. Improvement of image reconstruction is necessary for radiation dose reduction.

A Fine-Tuned Bidirectional Encoder Representations From Transformers Model for Food Named-Entity Recognition: Algorithm Development and Validation.

Journal of medical Internet research
BACKGROUND: Recently, food science has been garnering a lot of attention. There are many open research questions on food interactions, as one of the main environmental factors, with other health-related entities such as diseases, treatments, and drug...

Comparison of machine and deep learning for the classification of cervical cancer based on cervicography images.

Scientific reports
Cervical cancer is the second most common cancer in women worldwide with a mortality rate of 60%. Cervical cancer begins with no overt signs and has a long latent period, making early detection through regular checkups vitally immportant. In this stu...

Explainable DCNN based chest X-ray image analysis and classification for COVID-19 pneumonia detection.

Scientific reports
To speed up the discovery of COVID-19 disease mechanisms by X-ray images, this research developed a new diagnosis platform using a deep convolutional neural network (DCNN) that is able to assist radiologists with diagnosis by distinguishing COVID-19 ...