Diabetic retinopathy (DR) is a drastic disease. DR embarks on vision impairment when it is left undetected. In this article, learning-based techniques are presented for the segmentation and classification of DR lesions. The pre-trained Xception model is utilized for deep feature extraction in the segmentation phase. The extracted features are fed to Deeplabv3 for semantic segmentation. For the training of the segmentation model, an experiment is performed for the selection of the optimal hyperparameters that provided effective segmentation results in the testing phase. The multi-classification model is developed for feature extraction using the fully connected (FC) MatMul layer of efficient-net-b0 and pool-10 of the squeeze-net. The extracted features from both models are fused serially, having the dimension of N × 2020, amidst the best N × 1032 features chosen by applying the marine predictor algorithm (MPA). The multi-classification of the DR lesions into grades 0, 1, 2, and 3 is performed using neural network and KNN classifiers. The proposed method performance is validated on open access datasets such as DIARETDB1, e-ophtha-EX, IDRiD, and Messidor. The obtained results are better compared to those of the latest published works.

Diabetic retinopathy (DR) is a major reason of blindness around the world. The ophthalmologist manually analyzes the morphological alterations in veins of retina, and lesions in fundus images that is a time-taking, costly, and challenging procedure. It can be made easier with the assistance of computer aided diagnostic system (CADs) that are utilized for the diagnosis of DR lesions. Artificial intelligence (AI) based machine/deep learning methods performs vital role to increase the performance of the detection process, especially in the context of analyzing medical fundus images. In this paper, several current approaches of preprocessing, segmentation, feature extraction/selection, and classification are discussed for the detection of DR lesions. This survey paper also includes a detailed description of DR datasets that are accessible by the researcher for the identification of DR lesions. The existing methods limitations and challenges are also addressed, which will assist invoice researchers to start their work in this domain.

One of the major challenges in the formal verification of embedded system software is the complexity and substantially large size of the implementation. The problem becomes crucial when the embedded system is a complex medical device that is executing convoluted algorithms. In refinement-based verification, both specification and implementation are expressed as transition systems. Each behavior of the implementation transition system is matched to the specification transition system with the help of a refinement map. The refinement map can only project those values from the implementation which are responsible for labeling the current state of the system. When the refinement map is applied at the object code level, numerous instructions map to a single state in the specification transition system called stuttering instructions. We use the concept of Static Stuttering Abstraction (SSA) that filters the common multiple segments of stuttering instructions and replaces each segment with a merger. SSA algorithm reduces the implementation state space in embedded software, subsequently decreasing the efforts involved in manual verification with WEB refinement. The algorithm is formally proven for correctness. SSA is implemented on the pacemaker object code to evaluate the effectiveness of abstracted code in verification process. The results helped to establish the fact that, despite code size reduction, the bugs and errors can still be found. We implemented the SSA technique on two different platforms and it has been proven to be consistent in decreasing the code size significantly and hence the complexity of the implementation transition system. The results illustrate that there is considerable reduction in time and effort required for the verification of a complex software control, i.e., pacemaker when statically stuttering abstracted code is employed.