An adaptive weighting approach for ensemble-based detection of microaneurysms in color fundus images

A critical review on diagnosis of diabetic retinopathy using machine learning and deep learning

Diabetic Retinopathy (DR) is a health condition caused due to Diabetes Mellitus (DM). It causes vision problems and blindness due to disfigurement of human retina. According to statistics, 80% of diabetes patients battling from long diabetic period of 15 to 20 years, suffer from DR. Hence, it has become a dangerous threat to the health and life of people. To overcome DR, manual diagnosis of the disease is feasible but overwhelming and cumbersome at the same time and hence requires a revolutionary method. Thus, such a health condition necessitates primary recognition and diagnosis to prevent DR from developing into severe stages and prevent blindness. Innumerable Machine Learning (ML) models are proposed by researchers across the globe, to achieve this purpose. Various feature extraction techniques are proposed for extraction of DR features for early detection. However, traditional ML models have shown either meagre generalization throughout feature extraction and classification for deploying smaller datasets or consumes more of training time causing inefficiency in prediction while using larger datasets. Hence Deep Learning (DL), a new domain of ML, is introduced. DL models can handle a smaller dataset with help of efficient data processing techniques. However, they generally incorporate larger datasets for their deep architectures to enhance performance in feature extraction and image classification. This paper gives a detailed review on DR, its features, causes, ML models, state-of-the-art DL models, challenges, comparisons and future directions, for early detection of DR.

Automated microaneurysm detection method based on Eigenvalue analysis using Hessian matrix in retinal fundus images

Diabetic retinopathy (DR) is the most frequent cause of blindness. Microaneurysm (MA) is an early symptom of DR. Therefore, the detection of MA is important for the early detection of DR. We have proposed an automated MA detection method based on double-ring filter, but it has given many false positives. In this paper, we propose an MA detection method based on eigenvalue analysis using a Hessian matrix, with an aim to improve MA detection. After image preprocessing, the MA candidate regions were detected by eigenvalue analysis using the Hessian matrix in green-channeled retinal fundus images. Then, 126 features were calculated for each candidate region. By a threshold operation based on feature analysis, false positive candidates were removed. The candidate regions were then classified either as MA or false positive using artificial neural networks (ANN) based on principal component analysis (PCA). The 126 features were reduced to 25 components by PCA, and were then inputted to ANN. When the method was evaluated on visible MAs using 25 retinal images from the retinopathy online challenge (ROC) database, the true positive rate was 73%, with eight false positives per image.

Automated analysis of diabetic retinopathy images: principles, recent developments, and emerging trends

Diabetic retinopathy (DR) is a vision-threatening complication of diabetes. Timely diagnosis and intervention are essential for treatment that reduces the risk of vision loss. A good color retinal (fundus) photograph can be used as a surrogate for face-to-face evaluation of DR. The use of computers to assist or fully automate DR evaluation from retinal images has been studied for many years. Early work showed promising results for algorithms in detecting and classifying DR pathology. Newer techniques include those that adapt machine learning technology to DR image analysis. Challenges remain, however, that must be overcome before fully automatic DR detection and analysis systems become practical clinical tools.