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Found 6 papers, 2 papers with code
A Comprehensive Review of Artificial Intelligence Applications in Major Retinal Conditions
This paper provides a systematic survey of retinal diseases that cause visual impairments or blindness, emphasizing the importance of early detection for effective treatment.
An Incremental Learning Approach to Automatically Recognize Pulmonary Diseases from the Multi-vendor Chest Radiographs
Many researchers have utilized deep learning systems to diagnose pulmonary disorders using chest X-rays (CXRs).
Incremental Cross-Domain Adaptation for Robust Retinopathy Screening via Bayesian Deep Learning
Retinopathy represents a group of retinal diseases that, if not treated timely, can cause severe visual impairments or even blindness.
Clinically Verified Hybrid Deep Learning System for Retinal Ganglion Cells Aware Grading of Glaucomatous Progression
Significance: An RGC-aware system not only screens glaucoma but can also grade its severity and here we present an end-to-end solution that is thoroughly evaluated on a standardized dataset and is clinically validated for analyzing glaucomatous pathologies.
Physical Action Categorization using Signal Analysis and Machine Learning
Surface Electromyography (sEMG) presents a non-invasive mechanism through which we can translate the physical movement to signals for classification and use in applications.
Exploiting the Transferability of Deep Learning Systems Across Multi-modal Retinal Scans for Extracting Retinopathy Lesions
Retinal lesions play a vital role in the accurate classification of retinal abnormalities.
