Deep learning for diabetic retinopathy analysis: a review, research challenges, and future directions
Deep learning (DL) enables the creation of computational models comprising multiple
processing layers that learn data representations at multiple levels of abstraction. In the …
processing layers that learn data representations at multiple levels of abstraction. In the …
In-vivo and ex-vivo tissue analysis through hyperspectral imaging techniques: revealing the invisible features of cancer
In contrast to conventional optical imaging modalities, hyperspectral imaging (HSI) is able to
capture much more information from a certain scene, both within and beyond the visual …
capture much more information from a certain scene, both within and beyond the visual …
Automated pixel‐level pavement crack detection on 3D asphalt surfaces using a deep‐learning network
The CrackNet, an efficient architecture based on the Convolutional Neural Network (CNN),
is proposed in this article for automated pavement crack detection on 3D asphalt surfaces …
is proposed in this article for automated pavement crack detection on 3D asphalt surfaces …
Untangling computer-aided diagnostic system for screening diabetic retinopathy based on deep learning techniques
Diabetic Retinopathy (DR) is a predominant cause of visual impairment and loss.
Approximately 285 million worldwide population is affected with diabetes, and one-third of …
Approximately 285 million worldwide population is affected with diabetes, and one-third of …
Artificial intelligence in ophthalmology: a meta-analysis of deep learning models for retinal vessels segmentation
Background and Objective: Accurate retinal vessel segmentation is often considered to be a
reliable biomarker of diagnosis and screening of various diseases, including cardiovascular …
reliable biomarker of diagnosis and screening of various diseases, including cardiovascular …
A convolutional autoencoder approach for boosting the specificity of retinal blood vessels segmentation
N Nikoloulopoulou, I Perikos, I Daramouskas… - Applied sciences, 2023 - mdpi.com
Automated retina vessel segmentation of the human eye plays a vital role as it can
significantly assist ophthalmologists in identifying many eye diseases, such as diabetes …
significantly assist ophthalmologists in identifying many eye diseases, such as diabetes …
A retinal vessel detection approach based on shearlet transform and indeterminacy filtering on fundus images
A fundus image is an effective tool for ophthalmologists studying eye diseases. Retinal
vessel detection is a significant task in the identification of retinal disease regions. This study …
vessel detection is a significant task in the identification of retinal disease regions. This study …
Impact of retinal vessel image coherence on retinal blood vessel segmentation
Retinal vessel segmentation is critical in detecting retinal blood vessels for a variety of eye
disorders, and a consistent computerized method is required for automatic eye disorder …
disorders, and a consistent computerized method is required for automatic eye disorder …
Deep Error-Correcting Output Codes
Ensemble learning, online learning and deep learning are very effective and versatile in a
wide spectrum of problem domains, such as feature extraction, multi-class classification and …
wide spectrum of problem domains, such as feature extraction, multi-class classification and …
Local-Sensitive Connectivity Filter (LS-CF): A Post-Processing Unsupervised Improvement of the Frangi, Hessian and Vesselness Filters for Multimodal Vessel …
EO Rodrigues, LO Rodrigues, JHP Machado… - Journal of …, 2022 - mdpi.com
A retinal vessel analysis is a procedure that can be used as an assessment of risks to the
eye. This work proposes an unsupervised multimodal approach that improves the response …
eye. This work proposes an unsupervised multimodal approach that improves the response …