AIMC Topic: Diagnosis, Computer-Assisted

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Improving the performance of CNN to predict the likelihood of COVID-19 using chest X-ray images with preprocessing algorithms.

International journal of medical informatics
OBJECTIVE: This study aims to develop and test a new computer-aided diagnosis (CAD) scheme of chest X-ray images to detect coronavirus (COVID-19) infected pneumonia.

Automatic detection of non-perfusion areas in diabetic macular edema from fundus fluorescein angiography for decision making using deep learning.

Scientific reports
Vision loss caused by diabetic macular edema (DME) can be prevented by early detection and laser photocoagulation. As there is no comprehensive detection technique to recognize NPA, we proposed an automatic detection method of NPA on fundus fluoresce...

Comparison of performances of conventional and deep learning-based methods in segmentation of lung vessels and registration of chest radiographs.

Radiological physics and technology
Conventional machine learning-based methods have been effective in assisting physicians in making accurate decisions and utilized in computer-aided diagnosis for more than 30 years. Recently, deep learning-based methods, and convolutional neural netw...

Automatic deep learning-based colorectal adenoma detection system and its similarities with pathologists.

BMJ open
OBJECTIVES: The microscopic evaluation of slides has been gradually moving towards all digital in recent years, leading to the possibility for computer-aided diagnosis. It is worthwhile to know the similarities between deep learning models and pathol...

Personalized prediction of daily eczema severity scores using a mechanistic machine learning model.

Clinical and experimental allergy : journal of the British Society for Allergy and Clinical Immunology
BACKGROUND: Atopic dermatitis (AD) is a chronic inflammatory skin disease with periods of flares and remission. Designing personalized treatment strategies for AD is challenging, given the apparent unpredictability and large variation in AD symptoms ...

Fully-Connected Neural Networks with Reduced Parameterization for Predicting Histological Types of Lung Cancer from Somatic Mutations.

Biomolecules
Several challenges appear in the application of deep learning to genomic data. First, the dimensionality of input can be orders of magnitude greater than the number of samples, forcing the model to be prone to overfitting the training dataset. Second...

HyperKvasir, a comprehensive multi-class image and video dataset for gastrointestinal endoscopy.

Scientific data
Artificial intelligence is currently a hot topic in medicine. However, medical data is often sparse and hard to obtain due to legal restrictions and lack of medical personnel for the cumbersome and tedious process to manually label training data. The...

Clinically applicable histopathological diagnosis system for gastric cancer detection using deep learning.

Nature communications
The early detection and accurate histopathological diagnosis of gastric cancer increase the chances of successful treatment. The worldwide shortage of pathologists offers a unique opportunity for the use of artificial intelligence assistance systems ...

DZC DIAG: mobile application based on expert system to aid in the diagnosis of dengue, Zika, and chikungunya.

Medical & biological engineering & computing
Dengue, Zika, and chikungunya are epidemic diseases transmitted by the Aedes mosquito. These virus infections can be so severe to the point of bringing on mobility and neurological problems, or even death. Expert systems (ES) can be used as tools for...