DMENet: Diabetic Macular Edema diagnosis using Hierarchical Ensemble of CNNs

Abstract

Diabetic Macular Edema (DME) is an advanced stage of Diabetic Retinopathy (DR) and can lead to permanent vision loss. Currently, it affects 26.7 million people globally and on account of such a huge number of DME cases and the limited number of ophthalmologists, it is desirable to automate the diagnosis process. Computer-assisted, deep learning based diagnosis could help in early detection, following which precision medication can help to mitigate the vision loss. Method: In order to automate the screening of DME, we propose a novel DMENet Algorithm which is built on the pillars of Convolutional Neural Networks (CNNs). DMENet analyses the preprocessed color fundus images and passes it through a two-stage pipeline. The first stage detects the presence or absence of DME whereas the second stage takes only the positive cases and grades the images based on severity. In both the stages, we use a novel Hierarchical Ensemble of CNNs (HE-CNN). This paper uses two of the popular publicly available datasets IDRiD and MESSIDOR for classification. Preprocessing on the images is performed using morphological opening and gaussian kernel. The dataset is augmented to solve the class imbalance problem for better performance of the proposed model. Results: The proposed methodology achieved an average Accuracy of 96.12%, Sensitivity of 96.32%, Specificity of 95.84%, and F−1 score of 0.9609 on MESSI-DOR and IDRiD datasets. Conclusion: These excellent results establish the validity of the proposed methodology for use in DME screening and solidifies the applicability of the HE-CNN classification technique in the domain of biomedical imaging.

Automated Identification of Referable Retinal Pathology in Teleophthalmology Setting

“…[38][39][40][41] This paper introduces a CNN-based approach that enables fully automated retinal image classification into present or absent retinal pathology. Similar existing methods cannot be applied autonomously as they have not been developed while considering uninterpretable images, which are frequently encountered during eye screening, 2,3,[7][8][9][10][11][12][13][14][15][16][17][18][19] and thus cannot handle them well. By addressing these limitations, our approach facilitates the development of automated retinal diagnosis systems, where a healthcare worker does not need to evaluate the quality of the images (in order for some to be retaken) before they are submitted for the analysis.…”

“…However, to the best of our knowledge, no existing work can be deployed for fully automated retinal pathology diagnosis, mostly because uninterpretable images are excluded from training and testing. [2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21] This process requires an expert's input to determine ungradable images and exclude them from the dataset. The presence of images with substandard quality is universal and inevitable to encounter in clinical practice.…”

Review on the Role of Macular Edema in Retinopathy, Blindness and Automated Diagnosis Methods

“…Wang et al, 2016, proposed an improved level set algorithm for segmentation using linear configuration pattern (LCP) based features and detection of DME in the OCT image [62]. Singh and Gorantla, 2020, proposed a DMENet, which is a hierarchical ensemble CNN for detection of DME [63]. [68].…”

COVIDScreen: explainable deep learning framework for differential diagnosis of COVID-19 using chest X-rays

“…In the last few years, deep learning has grown exponentially and in the medical imaging world, the potential of automated disease discovery framework has been highlighted by many scientists [13,25,40,47,66,76]. Considering the success and potential of AI and deep learning in the medical imaging field, many computer scientists are exploring the possibility of automatic detection of COVID-19 using chest X-rays.…”