In the present exploration, our objective is to investigate the importance of Hall current coatings in the establishment of Cattaneo–Christov (CC) heat flux model in an unsteady aqueous-based nanofluid flow comprising single (SWCNTs) and multi-walled (MWCNTs) carbon nanotubes (CNTs) amid two parallel rotating stretchable disks. The novelty of the presented model is strengthened with the presence of homogeneous-heterogeneous (HH) reactions and thermal stratification effects. The numerical solution of the system of coupled differential equations with high nonlinearity is obtained by applying the bvp4c function of MATLAB software. To corroborate the authenticity of the present envisioned mathematical model, a comparison table is added to this study in limiting case. An excellent harmony between the two results is obtained. Effects of numerous parameters on involved distributions are displayed graphically and are argued logically in the light of physical laws. Numerical values of coefficient of drag force and Nusselt number are also tabulated for different parameters. It is observed that tangential velocity (function of rotation parameter) is increasing for both CNTs. Further, the incremental values of thermal stratification parameter cause the decrease in fluid temperature parameter.

Optical coherence tomography (OCT) is widely used to detect and classify retinal diseases. However, OCT-image-based manual detection by ophthalmologists is prone to errors and subjectivity. Thus, various automation methods have been proposed; however, improvements in detection accuracy are required. Particularly, automated techniques using deep learning on OCT images are being developed to detect various retinal disorders at an early stage. Here, we propose a deep learning-based automatic method for detecting and classifying retinal diseases using OCT images. The diseases include age-related macular degeneration, branch retinal vein occlusion, central retinal vein occlusion, central serous chorioretinopathy, and diabetic macular edema. The proposed method comprises four main steps: three pretrained models, DenseNet-201, InceptionV3, and ResNet-50, are first modified according to the nature of the dataset, after which the features are extracted via transfer learning. The extracted features are improved, and the best features are selected using ant colony optimization. Finally, the best features are passed to the k-nearest neighbors and support vector machine algorithms for final classification. The proposed method, evaluated using OCT retinal images collected from Soonchunhyang University Bucheon Hospital, demonstrates an accuracy of 99.1% with the incorporation of ACO. Without ACO, the accuracy achieved is 97.4%. Furthermore, the proposed method exhibits state-of-the-art performance and outperforms existing techniques in terms of accuracy.

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<abstract> <p>Optical coherence tomography (OCT) is a noninvasive, high-resolution imaging technique widely used in clinical practice to depict the structure of the retina. Over the past few decades, ophthalmologists have used OCT to diagnose, monitor, and treat retinal diseases. However, manual analysis of the complicated retinal layers using two colors, black and white, is time consuming. Although ophthalmologists have more experience, their results may be prone to erroneous diagnoses. Therefore, in this study, we propose an automatic method for diagnosing five retinal diseases based on the use of hybrid and ensemble deep learning (DL) methods. DL extracts a thousand constitutional features from images as features for training classifiers. The machine learning method classifies the extracted features and fuses the outputs of the two classifiers to improve classification performance. The distribution probabilities of two classifiers of the same class are aggregated; then, class prediction is made using the class with the highest probability. The limited dataset is resolved by the fine-tuning of classification knowledge and generating augmented images using transfer learning and data augmentation. Multiple DL models and machine learning classifiers are used to access a suitable model and classifier for the OCT images. The proposed method is trained and evaluated using OCT images collected from a hospital and exhibits a classification accuracy of 97.68% (InceptionResNetV2, ensemble: Extreme gradient boosting (XG-Boost) and k-nearest neighbor (k-NN). The experimental results show that our proposed method can improve the OCT classification performance; moreover, in the case of a limited dataset, the proposed method is critical to develop accurate classifications.</p> </abstract>

While the usage of digital ocular fundus image has been widespread in ophthalmology practice, the interpretation of the image has been still on the hands of the ophthalmologists which are quite costly. We explored a robust deep learning system that detects three major ocular diseases: diabetic retinopathy (DR), glaucoma (GLC), and age-related macular degeneration (AMD). The proposed method is composed of two steps. First, an initial quality evaluation in the classification system is proposed to filter out poorquality images to enhance its performance, a technique that has not been explored previously. Second, the transfer learning technique is used with various convolutional neural networks (CNN) models that automatically learn a thousand features in the digital retinal image, and are based on those features for diagnosing eye diseases. Comparison performance of many models is conducted to find the optimal model which fits with fundus classification. Among the different CNN models, DenseNet-201 outperforms others with an area under the receiver operating characteristic curve of 0.99. Furthermore, the corresponding specificities for healthy, DR, GLC, and AMD patients are found to be 89.52%, 96.69%, 89.58%, and 100%, respectively. These results demonstrate that the proposed method can reduce the time-consumption by automatically diagnosing multiple eye diseases using computer-aided assistance tools.