AIMC Topic: Diagnosis, Computer-Assisted

Clear Filters Showing 341 to 350 of 1778 articles

Deep Learning-Based Computer-Aided Diagnosis (CAD): Applications for Medical Image Datasets.

Sensors (Basel, Switzerland)
Computer-aided diagnosis (CAD) has proved to be an effective and accurate method for diagnostic prediction over the years. This article focuses on the development of an automated CAD system with the intent to perform diagnosis as accurately as possib...

Interpretability-Based Multimodal Convolutional Neural Networks for Skin Lesion Diagnosis.

IEEE transactions on cybernetics
Skin lesion diagnosis is a key step for skin cancer screening, which requires high accuracy and interpretability. Though many computer-aided methods, especially deep learning methods, have made remarkable achievements in skin lesion diagnosis, their ...

A systematic review on the use of explainability in deep learning systems for computer aided diagnosis in radiology: Limited use of explainable AI?

European journal of radiology
OBJECTIVES: This study aims to contribute to an understanding of the explainability of computer aided diagnosis studies in radiology that use end-to-end deep learning by providing a quantitative overview of methodological choices and by discussing th...

The Diagnosis of Developmental Dysplasia of the Hip From Hip Ultrasonography Images With Deep Learning Methods.

Journal of pediatric orthopedics
BACKGROUND: Hip ultrasonography is very important in the early diagnosis of developmental dysplasia of the hip. The application of deep learning-based medical image analysis to computer-aided diagnosis has the potential to provide decision-making sup...

Detection of arrhythmia in 12-lead varied-length ECG using multi-branch signal fusion network.

Physiological measurement
Automatic detection of arrhythmia based on electrocardiogram (ECG) plays a critical role in early prevention and diagnosis of cardiovascular diseases. With the increase in widely available digital ECG data and the development of deep learning, multi-...

A multi-feature deep learning system to enhance glaucoma severity diagnosis with high accuracy and fast speed.

Journal of biomedical informatics
Glaucoma is the leading cause of irreversible blindness, and the early detection and timely treatment are essential for glaucoma management. However, due to the interindividual variability in the characteristics of glaucoma onset, a single feature is...

Validation of Combined Deep Learning Triaging and Computer-Aided Diagnosis in 2901 Breast MRI Examinations From the Second Screening Round of the Dense Tissue and Early Breast Neoplasm Screening Trial.

Investigative radiology
OBJECTIVES: Computer-aided triaging (CAT) and computer-aided diagnosis (CAD) of screening breast magnetic resonance imaging have shown potential to reduce the workload of radiologists in the context of dismissing normal breast scans and dismissing be...

A Machine Learning Applied Diagnosis Method for Subcutaneous Cyst by Ultrasonography.

Oxidative medicine and cellular longevity
For decades, ultrasound images have been widely used in the detection of various diseases due to their high security and efficiency. However, reading ultrasound images requires years of experience and training. In order to support the diagnosis of cl...

A novel deep learning approach for sickle cell anemia detection in human RBCs using an improved wrapper-based feature selection technique in microscopic blood smear images.

Biomedizinische Technik. Biomedical engineering
Sickle Cell Anemia (SCA) is a disorder in Red Blood Cells (RBCs) of human blood. Children under five years and pregnant women are mostly affected by SCA. Early diagnosis of this ailment can save lives. In recent years, the computer aided diagnosis of...

A Deep Learning Method for Breast Cancer Classification in the Pathology Images.

IEEE journal of biomedical and health informatics
Breast cancer is the most common female cancer in the world, and it poses a huge threat to women's health. There is currently promising research concerning its early diagnosis using deep learning methodologies. However, some commonly used Convolution...