Automated Smartphone based System for Diagnosis of Diabetic Retinopathy

Early diagnosis of diabetic retinopathy for treatment of the disease has been failing to reach diabetic people living in rural areas. Shortage of trained ophthalmologists, limited availability of healthcare centers, and expensiveness of diagnostic equipment are among the reasons. Although many deep learning-based automatic diagnosis of diabetic retinopathy techniques have been implemented in the literature, these methods still fail to provide a point-of-care diagnosis. This raises the need for an independent diagnostic of diabetic retinopathy that can be used by a non-expert. Recently the usage of smartphones has been increasing across the world. Automated diagnoses of diabetic retinopathy can be deployed on smartphones in order to provide an instant diagnosis to diabetic people residing in remote areas. In this paper, inception based convolutional neural network and binary decision tree-based ensemble of classifiers have been proposed and implemented to detect and classify diabetic retinopathy. The proposed method was further imported into a smartphone application for mobile-based classification, which provides an offline and automatic system for diagnosis of diabetic retinopathy.Comment: 12 pages, 4 figures, 4 tables, 1 appendix. Copyright \copyright 2019, IEEE. Published in: 2019 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS

Deep learning for diabetic retinopathy detection and classification based on fundus images: A review.

Diabetic Retinopathy is a retina disease caused by diabetes mellitus and it is the leading cause of blindness globally. Early detection and treatment are necessary in order to delay or avoid vision deterioration and vision loss. To that end, many artificial-intelligence-powered methods have been proposed by the research community for the detection and classification of diabetic retinopathy on fundus retina images. This review article provides a thorough analysis of the use of deep learning methods at the various steps of the diabetic retinopathy detection pipeline based on fundus images. We discuss several aspects of that pipeline, ranging from the datasets that are widely used by the research community, the preprocessing techniques employed and how these accelerate and improve the models' performance, to the development of such deep learning models for the diagnosis and grading of the disease as well as the localization of the disease's lesions. We also discuss certain models that have been applied in real clinical settings. Finally, we conclude with some important insights and provide future research directions

Automatic segmentation of exudates in colour retinal fundus images

This work aims at the development of an algorithm that allows the automatic detection of exudates in retinal fundus images. The detection of exudates allows diabetic retinopathy (DR) to be diagnosed, consequently it is an important task for the control and the treatment of people suffering DR. In addition, an increase of 35\% of people suffering from diabetes is predicted and, therefore, of people who will suffer from DR in the coming years. As a result, an important burden for ophthalmologists will be expected. For all this, it's highly needed the development of an automatic system for the detection of exudates. Two different algorithms are proposed. Background subtraction to deal with uneven illumination and mathematical morphology operators are used for exudate location. Finally, dynamic thresholding is applied for exudate segmentation. In the first algorithm dynamic thresholding is combined with the Kirsch edge detector. In the second one, a template and morphological operators are used to differentiate bright elements from exudates is used. The methods have been validated in three public datasets named e-ophta-EX, HEI-MED and DiaretDB1. The first two datasets have been used to validated the algorithms both at lesion level and image-level. However, DiaretDB1 was only used to validate the algorithms at image-level due to its ground truth does not mark exact boundaries of exudates. The results for the image-level validation are better for the second algorithm obtaining an AUC of 0.84, 0.75 and 0.84 for e-ophta-EX, HEI-MED and DiaretDB1, respectively. The results obtained with the evaluation at lesion-level are the same for the two methods and are quantified in terms of sensitivity and PPV. We have achieved values of sensitivity and PPV of 0.54 and 0.52, respectively, in e-ophta-EX and, 0.52 and 0.52, respectively, in HEI-MED for method 1. For method 2, we have obtained values for sensitivity and PPV of 0.5 and 0.57, respectively, for e-ophta-EX and 0.42 and 0.76, respectively, for HEI-MED.Outgoin

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 recent past, the use of deep learning has been proliferating, yielding promising results in applications across a growing number of fields, most notably in image processing, medical image analysis, data analysis, and bioinformatics. DL algorithms have also had a significant positive impact through yielding improvements in screening, recognition, segmentation, prediction, and classification applications across different domains of healthcare, such as those concerning the abdomen, cardiac, pathology, and retina. Given the extensive body of recent scientific contributions in this discipline, a comprehensive review of deep learning developments in the domain of diabetic retinopathy (DR) analysis, viz., screening, segmentation, prediction, classification, and validation, is presented here. A critical analysis of the relevant reported techniques is carried out, and the associated advantages and limitations highlighted, culminating in the identification of research gaps and future challenges that help to inform the research community to develop more efficient, robust, and accurate DL models for the various challenges in the monitoring and diagnosis of DR

Transfer Learning Based Fault Detection for Suspension System Using Vibrational Analysis and Radar Plots

The suspension system is of paramount importance in any automobile. Thanks to the suspension system, every journey benefits from pleasant rides, stable driving and precise handling. However, the suspension system is prone to faults that can significantly impact the driving quality of the vehicle. This makes it essential to find and diagnose any faults in the suspension system and rectify them immediately. Numerous techniques have been used to identify and diagnose suspension faults, each with drawbacks. This paper’s proposed suspension fault detection system aims to detect these faults using deep transfer learning techniques instead of the time-consuming and expensive conventional methods. This paper used pre-trained networks such as Alex Net, ResNet-50, Google Net and VGG16 to identify the faults using radar plots of the vibration signals generated by the suspension system in eight cases. The vibration data were acquired using an accelerometer and data acquisition system placed on a test rig for eight different test conditions (seven faulty, one good). The deep learning model with the highest accuracy in identifying and detecting faults among the four models was chosen and adopted to find defects. The results state that VGG16 produced the highest classification accuracy of 96.70%