Recent advances in digital multimedia tampering detection for forensics analysis
In the digital multimedia era, digital forensics is becoming an emerging area of research
thanks to the large amount of image and video files generated. Ensuring the integrity of such …
thanks to the large amount of image and video files generated. Ensuring the integrity of such …
A survey on medical image analysis in diabetic retinopathy
S Stolte, R Fang - Medical image analysis, 2020 - Elsevier
Diabetic Retinopathy (DR) represents a highly-prevalent complication of diabetes in which
individuals suffer from damage to the blood vessels in the retina. The disease manifests …
individuals suffer from damage to the blood vessels in the retina. The disease manifests …
Network anomaly intrusion detection using a nonparametric Bayesian approach and feature selection
Anomaly-based intrusion detection systems (IDSs) have been deployed to monitor network
activity and to protect systems and the Internet of Things (IoT) devices from attacks (or …
activity and to protect systems and the Internet of Things (IoT) devices from attacks (or …
[PDF][PDF] Deep learning approach for stages of severity classification in diabetic retinopathy using color fundus retinal images
Diabetes is a very fast-growing disease in India, with currently more than 72 million patients.
Prolonged diabetes (about almost 20 years) can cause serious loss to the tiny blood vessels …
Prolonged diabetes (about almost 20 years) can cause serious loss to the tiny blood vessels …
Online learning of finite and infinite gamma mixture models for COVID-19 detection in medical images
The accurate detection of abnormalities in medical images (like X-ray and CT scans) is a
challenging problem due to images' blurred boundary contours, different sizes, variable …
challenging problem due to images' blurred boundary contours, different sizes, variable …
Markov chain Monte Carlo-based Bayesian inference for learning finite and infinite inverted beta-Liouville mixture models
Recently Inverted Beta-Liouville mixture models have emerged as an efficient paradigm for
proportional positive vectors modeling and unsupervised learning. However, little attention …
proportional positive vectors modeling and unsupervised learning. However, little attention …
Discriminative learning approach based on flexible mixture model for medical data categorization and recognition
In this paper, we propose a novel hybrid discriminative learning approach based on shifted-
scaled Dirichlet mixture model (SSDMM) and Support Vector Machines (SVMs) to address …
scaled Dirichlet mixture model (SSDMM) and Support Vector Machines (SVMs) to address …
Towards explainable deep neural networks for the automatic detection of diabetic retinopathy
HS Alghamdi - Applied Sciences, 2022 - mdpi.com
Featured Application The proposed approach can be applied to any of the Convolutional
Neural Networks-based architecture to explain, evaluate and validate the model's decisions …
Neural Networks-based architecture to explain, evaluate and validate the model's decisions …
Bayesian inference framework for bounded generalized Gaussian‐based mixture model and its application to biomedical images classification
Biomedical image classification problem has attracted a lot of attention in medical
engineering community and medicine applications. Accurate and automatic classification …
engineering community and medicine applications. Accurate and automatic classification …
Retinal image classification by self-supervised fuzzy clustering network
Y Luo, J Pan, S Fan, Z Du, G Zhang - IEEE Access, 2020 - ieeexplore.ieee.org
Diabetic retinal image classification aims to conduct diabetic retinopathy automatically
diagnosing, which has achieved considerable improvement by deep learning models …
diagnosing, which has achieved considerable improvement by deep learning models …