Related papers: Automatic Chronic Degenerative Diseases Identifica…
There is a rising need for computational models that can complementarily leverage data of different modalities while investigating associations between subjects for population-based disease analysis. Despite the success of convolutional…
Investigation on the electrocardiogram (ECG) signals is an essential way to diagnose heart disease since the ECG process is noninvasive and easy to use. This work presents a supraventricular arrhythmia prediction model consisting of a few…
Parkinson's disease (PD), a severe and progressive neurological illness, affects millions of individuals worldwide. For effective treatment and management of PD, an accurate and early diagnosis is crucial. This study presents a deep…
Arrhythmia is a cardiovascular disease that manifests irregular heartbeats. In arrhythmia detection, the electrocardiogram (ECG) signal is an important diagnostic technique. However, manually evaluating ECG signals is a complicated and…
Deep learning methods based on Convolutional Neural Networks (CNNs) have shown great potential to improve early and accurate diagnosis of Alzheimer's disease (AD) dementia based on imaging data. However, these methods have yet to be widely…
Purpose: To design multi-disease classifiers for body CT scans for three different organ systems using automatically extracted labels from radiology text reports.Materials & Methods: This retrospective study included a total of 12,092…
Understanding the determinants underlying variations in urban health status is important for informing urban design and planning, as well as public health policies. Multiple heterogeneous urban features could modulate the prevalence of…
Deep learning has significantly advanced electrocardiogram (ECG) analysis, enabling automatic annotation, disease screening, and prognosis beyond traditional clinical capabilities. However, understanding these models remains a challenge,…
Chronic diseases are long-lasting conditions that require lifelong medical attention. Using big EMR data, we have developed early disease risk prediction models for five common chronic diseases: diabetes, hypertension, CKD, COPD, and…
Machine learning (ML) methods have the potential to automate clinical EEG analysis. They can be categorized into feature-based (with handcrafted features), and end-to-end approaches (with learned features). Previous studies on EEG pathology…
This paper study provides a novel contribution to the field of signal processing and DL for ECG signal analysis by introducing a new feature representation method for ECG signals. The proposed method is based on transforming time frequency…
Alzheimer's disease detection requires expensive neuroimaging or invasive procedures, limiting accessibility. This study explores whether deep learning can enable non-invasive Alzheimer's disease detection through handwriting analysis.…
The shape of erythrocytes or red blood cells is altered in several pathological conditions. Therefore, identifying and quantifying different erythrocyte shapes can help diagnose various diseases and assist in designing a treatment strategy.…
Deep convolutional neural networks(CNNs) have been successful for a wide range of computer vision tasks, including image classification. A specific area of the application lies in digital pathology for pattern recognition in the…
Age-Related Macular Degeneration (AMD) is an asymptomatic retinal disease which may result in loss of vision. There is limited access to high-quality relevant retinal images and poor understanding of the features defining sub-classes of…
Motor dysfunction is a common sign of neurodegenerative diseases (NDs) such as Parkinson's disease (PD) and Alzheimer's disease (AD), but may be difficult to detect, especially in the early stages. In this work, we examine the behavior of a…
In recent years, deep learning based methods have shown success in essential medical image analysis tasks such as segmentation. Post-processing and refining the results of segmentation is a common practice to decrease the misclassifications…
Alzheimer's disease (AD), a degenerative brain condition, can benefit from early prediction to slow its progression. As the disease progresses, patients typically undergo brain atrophy. Current prediction methods for Alzheimers disease…
Medical diagnoses can shape and change the life of a person drastically. Therefore, it is always best advised to collect as much evidence as possible to be certain about the diagnosis. Unfortunately, in the case of the Brugada Syndrome…
The use of Deep Learning (DL) based methods in medical histopathology images have been one of the most sought after solutions to classify, segment, and detect diseased biopsy samples. However, given the complex nature of medical datasets…