AIMC Topic: Deep Learning

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Classification by a stacking model using CNN features for COVID-19 infection diagnosis.

Journal of X-ray science and technology
Affecting millions of people all over the world, the COVID-19 pandemic has caused the death of hundreds of thousands of people since its beginning. Examinations also found that even if the COVID-19 patients initially survived the coronavirus, pneumon...

Brain Tumor Detection Using Machine Learning and Deep Learning: A Review.

Current medical imaging
According to the International Agency for Research on Cancer (IARC), the mortality rate due to brain tumors is 76%. It is required to detect the brain tumors as early as possible and to provide the patient with the required treatment to avoid any fat...

A Novel Multicolor-thresholding Auto-detection Method to Detect the Location and Severity of Inflammation in Confirmed SARS-COV-2 Cases using Chest X-Ray Images.

Current medical imaging
OBJECTIVES: Since late 2019, Coronavirus Disease 2019 (COVID-19) has spread around the world. It has been determined that the disease is very contagious and can cause Acute Respiratory Distress (ARD). Medical imaging has the potential to help identif...

Advanced Deep Learning Algorithms for Infectious Disease Modeling Using Clinical Data: A Case Study on COVID-19.

Current medical imaging
BACKGROUND: Dealing with the COVID-19 pandemic has been one of the most important objectives of many countries.Intently observing the growth dynamics of the cases is one way to accomplish the solution for the pandemic.

Quantitative Comparisons of Deep-learning-based and Atlas-based Auto- segmentation of the Intermediate Risk Clinical Target Volume for Nasopharyngeal Carcinoma.

Current medical imaging
BACKGROUND: Manual segment target volumes were time-consuming and inter-observer variability couldn't be avoided. With the development of computer science, auto-segmentation had the potential to solve this problem.

Impact of Cystic Fibrosis Transmembrane Conductance Regulator Therapy on Chronic Rhinosinusitis and Health Status: Deep Learning CT Analysis and Patient-reported Outcomes.

Annals of the American Thoracic Society
Elexacaftor, tezacaftor, and ivacaftor (ETI) in triple combination improves pulmonary health for people with cystic fibrosis (PwCF). However, its impact on objective measures of sinus disease and health utility is unestablished. To evaluate the imp...

A MULTITASK DEEP-LEARNING SYSTEM FOR ASSESSMENT OF DIABETIC MACULAR ISCHEMIA ON OPTICAL COHERENCE TOMOGRAPHY ANGIOGRAPHY IMAGES.

Retina (Philadelphia, Pa.)
PURPOSE: We aimed to develop and test a deep-learning system to perform image quality and diabetic macular ischemia (DMI) assessment on optical coherence tomography angiography (OCTA) images.

ANALYSIS OF TRANSFER LEARNING FOR SELECT RETINAL DISEASE CLASSIFICATION.

Retina (Philadelphia, Pa.)
PURPOSE: To analyze the effect of transfer learning for classification of diabetic retinopathy (DR) by fundus photography and select retinal diseases by spectral domain optical coherence tomography (SD-OCT).

EVALUATION OF ARTIFICIAL INTELLIGENCE-BASED QUANTITATIVE ANALYSIS TO IDENTIFY CLINICALLY SIGNIFICANT SEVERE RETINOPATHY OF PREMATURITY.

Retina (Philadelphia, Pa.)
PURPOSE: To evaluate the screening potential of a deep learning algorithm-derived severity score by determining its ability to detect clinically significant severe retinopathy of prematurity (ROP).

Mathematical and deep learning analysis based on tissue dielectric properties at low frequencies predict outcome in human breast cancer.

Technology and health care : official journal of the European Society for Engineering and Medicine
BACKGROUND: The early detection of human breast cancer represents a great chance of survival. Malignant tissues have more water content and higher electrolytes concentration while they have lower fat content than the normal. These cancer biochemical ...