High-resolution CT image analysis based on 3D convolutional neural network can enhance the classification performance of radiologists in classifying pulmonary non-solid nodules

Super Resolution is a field of image analysis that focuses on boosting the resolution of photographs and movies without compromising detail or visual appeal, instead enhancing both. Multiple (many input images and one output image) or single (one input and one output) stages are used to convert low-resolution photos to high-resolution photos. The study examines super-resolution methods based on a convolutional neural network (CNN) for super-resolution mapping at the sub-pixel level, as well as its primary characteristics and limitations for noisy or medical images.

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Survey on CNN based super resolution methods

Journal La Multiapp ◽

10.37899/journallamultiapp.v1i4.444 ◽

2021 ◽

Vol 1 (4) ◽

pp. 27-33

Author(s):

Rafaa Amen Kazem ◽

Jamila H. Suad ◽

Huda Abdulaali Abdulbaqi

Keyword(s):

Neural Network ◽

Image Analysis ◽

High Resolution ◽

Convolutional Neural Network ◽

Medical Images ◽

Super Resolution ◽

Low Resolution ◽

Resolution Mapping ◽

Super Resolution Mapping ◽

Output Stages

Super Resolution is a field of image analysis that focuses on boosting the resolution of photographs and movies without compromising detail or visual appeal, instead enhancing both. Multiple (many input images and one output image) or single (one input and one output) stages are used to convert low-resolution photos to high-resolution photos. The study examines super-resolution methods based on a convolutional neural network (CNN) for super-resolution mapping at the sub-pixel level, as well as its primary characteristics and limitations for noisy or medical images.

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CT Image Analysis and Clinical Diagnosis of New Coronary Pneumonia Based on Improved Convolutional Neural Network

Computational and Mathematical Methods in Medicine ◽

10.1155/2021/7259414 ◽

2021 ◽

Vol 2021 ◽

pp. 1-12

Author(s):

Wu Deng ◽

Bo Yang ◽

Wei Liu ◽

Weiwei Song ◽

Yuan Gao ◽

...

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Deep Neural Networks ◽

Ct Image ◽

Lesion Area ◽

Abstract Representation ◽

Adversarial Network ◽

Depth Analysis ◽

Image Diagnosis ◽

Ct Image Analysis

In this paper, based on the improved convolutional neural network, in-depth analysis of the CT image of the new coronary pneumonia, using the U-Net series of deep neural networks to semantically segment the CT image of the new coronary pneumonia, to obtain the new coronary pneumonia area as the foreground and the remaining areas as the background of the binary image, provides a basis for subsequent image diagnosis. Secondly, the target-detection framework Faster RCNN extracts features from the CT image of the new coronary pneumonia tumor, obtains a higher-level abstract representation of the data, determines the lesion location of the new coronary pneumonia tumor, and gives its bounding box in the image. By generating an adversarial network to diagnose the lesion area of the CT image of the new coronary pneumonia tumor, obtaining a complete image of the new coronary pneumonia, achieving the effect of the CT image diagnosis of the new coronary pneumonia tumor, and three-dimensionally reconstructing the complete new coronary pneumonia model, filling the current the gap in this aspect, provide a basis to produce new coronary pneumonia prosthesis and improve the accuracy of diagnosis.

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A Multi-Center Study of COVID-19 Patient Prognosis Using Deep Learning-based CT Image Analysis and Electronic Health Records

European Journal of Radiology ◽

10.1016/j.ejrad.2021.109583 ◽

2021 ◽

pp. 109583

Author(s):

Kuang Gong ◽

Dufan Wu ◽

Chiara Daniela Arru ◽

Fatemeh Homayounieh ◽

Nir Neumark ◽

...

Keyword(s):

Image Analysis ◽

Deep Learning ◽

Electronic Health Records ◽

Ct Image ◽

Health Records ◽

Electronic Health ◽

Ct Image Analysis ◽

Patient Prognosis ◽

Multi Center Study ◽

Center Study

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Validating Deep Neural Networks for Online Decoding of Motor Imagery Movements from EEG Signals

Sensors ◽

10.3390/s19010210 ◽

2019 ◽

Vol 19 (1) ◽

pp. 210 ◽

Cited By ~ 32

Author(s):

Zied Tayeb ◽

Juri Fedjaev ◽

Nejla Ghaboosi ◽

Christoph Richter ◽

Lukas Everding ◽

...

Keyword(s):

Neural Network ◽

Machine Learning ◽

Deep Learning ◽

Convolutional Neural Network ◽

Motor Imagery ◽

Classification Performance ◽

Feature Engineering ◽

Learning Models ◽

Eeg Signals ◽

Learning Methods

Non-invasive, electroencephalography (EEG)-based brain-computer interfaces (BCIs) on motor imagery movements translate the subject’s motor intention into control signals through classifying the EEG patterns caused by different imagination tasks, e.g., hand movements. This type of BCI has been widely studied and used as an alternative mode of communication and environmental control for disabled patients, such as those suffering from a brainstem stroke or a spinal cord injury (SCI). Notwithstanding the success of traditional machine learning methods in classifying EEG signals, these methods still rely on hand-crafted features. The extraction of such features is a difficult task due to the high non-stationarity of EEG signals, which is a major cause by the stagnating progress in classification performance. Remarkable advances in deep learning methods allow end-to-end learning without any feature engineering, which could benefit BCI motor imagery applications. We developed three deep learning models: (1) A long short-term memory (LSTM); (2) a spectrogram-based convolutional neural network model (CNN); and (3) a recurrent convolutional neural network (RCNN), for decoding motor imagery movements directly from raw EEG signals without (any manual) feature engineering. Results were evaluated on our own publicly available, EEG data collected from 20 subjects and on an existing dataset known as 2b EEG dataset from “BCI Competition IV”. Overall, better classification performance was achieved with deep learning models compared to state-of-the art machine learning techniques, which could chart a route ahead for developing new robust techniques for EEG signal decoding. We underpin this point by demonstrating the successful real-time control of a robotic arm using our CNN based BCI.

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