0000000000130619
AUTHOR
Anneke Annassia Putri Siswadi
Computer-aided-diagnosis for ocular abnormalities from a single color fundus photography with deep learning
Any damage to the retina can lead to severe consequences like blindness. This visual impairment is preventable by early detection of ocular abnormalities. Computer-aided diagnosis (CAD) for ocular abnormalities is built by analyzing retinal imaging modalities, for instance, Color Fundus Photography (CFP). The main objectives of this thesis are to build two CAD models, one to detect the microaneurysms (MAs), the first visible symptom of diabetic retinopathy, and the other for multi-label detection of 28 ocular abnormalities consisting of frequent and rare abnormalities from a single CFP by using deep learning-based approaches. Two methods were proposed for MAs detection: ensemble-based and c…
A Survey on Microaneurysms Detection in Color Fundus Images
Early Detection of Microaneurysms (MA) plays a vital role in preventing the blindness caused by diabetic retinopathy (DR). DR is preventable yet a serious diabetic problem. Treatment at an earlier stage reduces the risk of blindness. Microaneurysm is the first sign of DR found in fundus images while doing screening. Detection of MA is a challenging task mainly because of its size. MA appears as a tiny red spot ranging from 15µm to 60µm size. The most common way to detect the MA from a colour fundus image is by classification/segmentation through machine learning and deep learning approaches. The FROC-based performance evaluation shows that the existing methods can reach only up to 80% of se…