AIMC Topic: Fundus Oculi

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Segmentation and Classification of Glaucoma Using U-Net with Deep Learning Model.

Journal of healthcare engineering
Glaucoma is the second most common cause for blindness around the world and the third most common in Europe and the USA. Around 78 million people are presently living with glaucoma (2020). It is expected that 111.8 million people will have glaucoma b...

Detection of microscopic glaucoma through fundus images using deep transfer learning approach.

Microscopy research and technique
Glaucoma disease in humans can lead to blindness if it progresses to the point where it affects the oculus' optic nerve head. It is not easily detected since there are no symptoms, but it can be detected using tonometry, ophthalmoscopy, and perimeter...

Deep learning model using retinal vascular images for classifying schizophrenia.

Schizophrenia research
Contemporary psychiatric diagnosis still relies on the subjective symptom report of the patient during a clinical interview by a psychiatrist. Given the significant variability in personal reporting and differences in the skill set of psychiatrists, ...

Deep learning-based classification of retinal vascular diseases using ultra-widefield colour fundus photographs.

BMJ open ophthalmology
OBJECTIVE: To assess the ability of a deep learning model to distinguish between diabetic retinopathy (DR), sickle cell retinopathy (SCR), retinal vein occlusions (RVOs) and healthy eyes using ultra-widefield colour fundus photography (UWF-CFP).

Genetic U-Net: Automatically Designed Deep Networks for Retinal Vessel Segmentation Using a Genetic Algorithm.

IEEE transactions on medical imaging
Recently, many methods based on hand-designed convolutional neural networks (CNNs) have achieved promising results in automatic retinal vessel segmentation. However, these CNNs remain constrained in capturing retinal vessels in complex fundus images....

Asymmetry between right and left fundus images identified using convolutional neural networks.

Scientific reports
We analyzed fundus images to identify whether convolutional neural networks (CNNs) can discriminate between right and left fundus images. We gathered 98,038 fundus photographs from the Gyeongsang National University Changwon Hospital, South Korea, an...

Deep learning model calibration for improving performance in class-imbalanced medical image classification tasks.

PloS one
In medical image classification tasks, it is common to find that the number of normal samples far exceeds the number of abnormal samples. In such class-imbalanced situations, reliable training of deep neural networks continues to be a major challenge...

Multiple instance learning detects peripheral arterial disease from high-resolution color fundus photography.

Scientific reports
Peripheral arterial disease (PAD) is caused by atherosclerosis and is a common disease of the elderly leading to excess morbidity and mortality. Early PAD diagnosis is important, as the only available causal therapy is addressing risk factors like sm...

End-to-end diabetic retinopathy grading based on fundus fluorescein angiography images using deep learning.

Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie
PURPOSE: To develop and validate a deep learning system for diabetic retinopathy (DR) grading based on fundus fluorescein angiography (FFA) images.

Microscopic retinal blood vessels detection and segmentation using support vector machine and K-nearest neighbors.

Microscopy research and technique
The retina is the deepest layer of texture covering the rear of the eye, recorded by fundus images. Vessel detection and segmentation are useful in disease diagnosis. The retina's blood vessels could help diagnose maladies such as glaucoma, diabetic ...