AIMC Topic: Diagnosis, Computer-Assisted

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A Review of Machine Learning Techniques for Keratoconus Detection and Refractive Surgery Screening.

Seminars in ophthalmology
Various machine learning techniques have been developed for keratoconus detection and refractive surgery screening. These techniques utilize inputs from a range of corneal imaging devices and are built with automated decision trees, support vector ma...

IAPSO-AIRS: A novel improved machine learning-based system for wart disease treatment.

Journal of medical systems
Wart disease (WD) is a skin illness on the human body which is caused by the human papillomavirus (HPV). This study mainly concentrates on common and plantar warts. There are various treatment methods for this disease, including the popular immunothe...

An automatic method for lung segmentation and reconstruction in chest X-ray using deep neural networks.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Chest X-ray (CXR) is one of the most used imaging techniques for detection and diagnosis of pulmonary diseases. A critical component in any computer-aided system, for either detection or diagnosis in digital CXR, is the auto...

A review on brain tumor diagnosis from MRI images: Practical implications, key achievements, and lessons learned.

Magnetic resonance imaging
The successful early diagnosis of brain tumors plays a major role in improving the treatment outcomes and thus improving patient survival. Manually evaluating the numerous magnetic resonance imaging (MRI) images produced routinely in the clinic is a ...

Autoencoding of long-term scalp electroencephalogram to detect epileptic seizure for diagnosis support system.

Computers in biology and medicine
INTRODUCTION: Epileptologists could benefit from a diagnosis support system that automatically detects seizures because visual inspection of long-term electroencephalograms (EEGs) is extremely time-consuming. However, the diversity of seizures among ...

Deep Learning-Assisted Diagnosis of Cerebral Aneurysms Using the HeadXNet Model.

JAMA network open
IMPORTANCE: Deep learning has the potential to augment clinician performance in medical imaging interpretation and reduce time to diagnosis through automated segmentation. Few studies to date have explored this topic.

VesselNet: A deep convolutional neural network with multi pathways for robust hepatic vessel segmentation.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Extraction or segmentation of organ vessels is an important task for surgical planning and computer-aided diagnoses. This is a challenging task due to the extremely small size of the vessel structure, low SNR, and varying contrast in medical image da...

Improving Workflow Efficiency for Mammography Using Machine Learning.

Journal of the American College of Radiology : JACR
OBJECTIVE: The aim of this study was to determine whether machine learning could reduce the number of mammograms the radiologist must read by using a machine-learning classifier to correctly identify normal mammograms and to select the uncertain and ...

A novel IRBF-RVM model for diagnosis of atrial fibrillation.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Atrial fibrillation (AF) is one of the common cardiovascular diseases, and electrocardiography (ECG) is a key indicator for the detection and diagnosis of AF and other heart diseases. In this study, an improved machine learn...