Automatic Cataract Detection Using the Convolutional Neural Network and Digital Camera Images

Background: The cataract is the most prevalent cause of blindness worldwide and is responsible for more than 51 % of blindness cases. As the treatment process is becoming smart and the burden of ophthalmologists is reducing, many existing systems have adopted machine-learning-based cataract classification methods with manual extraction of data features. However, the manual extraction of retinal features is generally time-consuming and exhausting and requires skilled ophthalmologists. Material and Methods: Convolutional neural network (CNN) is a highly common automatic feature extraction model which, compared to machine learning approaches, requires much larger datasets to avoid overfitting issues. This article designs a deep convolutional network for automatic cataract recognition in healthy eyes. The algorithm consists of four convolution layers and a fully connected layer for hierarchical feature learning and training. Results: The proposed approach was tested on collected images and indicated an 90.88 % accuracy on testing data. The keras model provides a function that evaluates the model, which is equal to the value of 84.14 %, the model can be further developed and improved to be applied for the automatic recognition and treatment of ocular diseases. Conclusion: This study presented a deep learning algorithm for the automatic recognition of healthy eyes from cataractous ones. The results suggest that the proposed scheme outperforms other conventional methods and can be regarded as a reference for other retinal disorders

An Image Quality Selection and Effective Denoising on Retinal Images Using Hybrid Approaches

Retinal image analysis has remained an essential topic of research in the last decades. Several algorithms and techniques have been developed for the analysis of retinal images. Most of these techniques use benchmark retinal image datasets to evaluate performance without first exploring the quality of the retinal image. Hence, the performance metrics evaluated by these approaches are uncertain. In this paper, the quality of the images is selected by utilizing the hybrid naturalness image quality evaluator and the perception-based image quality evaluator (hybrid NIQE-PIQE) approach. Here, the raw input image quality score is evaluated using the Hybrid NIQE-PIQE approach. Based on the quality score value, the deep learning convolutional neural network (DCNN) categorizes the images into low quality, medium quality and high quality images. Then the selected quality images are again pre-processed to remove the noise present in the images. The individual green channel (G-channel) is extracted from the selected quality RGB images for noise filtering. Moreover, hybrid modified histogram equalization and homomorphic filtering (Hybrid G-MHE-HF) are utilized for enhanced noise filtering. The implementation of proposed scheme is implemented on MATLAB 2021a. The performance of the implemented method is compared with the other approaches to the accuracy, sensitivity, specificity, precision and F-score on DRIMDB and DRIVE datasets. The proposed scheme’s accuracy is 0.9774, sensitivity is 0.9562, precision is 0.99, specificity is 0.99, and F-measure is 0.9776 on the DRIMDB dataset, respectively

Deep learning of the retina enables phenome- and genome-wide analyses of the microvasculature.

Background: The microvasculature, the smallest blood vessels in the body, has key roles in maintenance of organ health as well as tumorigenesis. The retinal fundus is a window for human in vivo non-invasive assessment of the microvasculature. Large-scale complementary machine learning-based assessment of the retinal vasculature with phenome-wide and genome-wide analyses may yield new insights into human health and disease. Methods: We utilized 97,895 retinal fundus images from 54,813 UK Biobank participants. Using convolutional neural networks to segment the retinal microvasculature, we calculated fractal dimension (FD) as a measure of vascular branching complexity, and vascular density. We associated these indices with 1,866 incident ICD-based conditions (median 10y follow-up) and 88 quantitative traits, adjusting for age, sex, smoking status, and ethnicity. Results: Low retinal vascular FD and density were significantly associated with higher risks for incident mortality, hypertension, congestive heart failure, renal failure, type 2 diabetes, sleep apnea, anemia, and multiple ocular conditions, as well as corresponding quantitative traits. Genome-wide association of vascular FD and density identified 7 and 13 novel loci respectively, which were enriched for pathways linked to angiogenesis (e.g., VEGF, PDGFR, angiopoietin, and WNT signaling pathways) and inflammation (e.g., interleukin, cytokine signaling). Conclusions: Our results indicate that the retinal vasculature may serve as a biomarker for future cardiometabolic and ocular disease and provide insights on genes and biological pathways influencing microvascular indices. Moreover, such a framework highlights how deep learning of images can quantify an interpretable phenotype for integration with electronic health records, biomarker, and genetic data to inform risk prediction and risk modification

Biomedical Data Classification with Improvised Deep Learning Architectures

With the rise of very powerful hardware and evolution of deep learning architectures, healthcare data analysis and its applications have been drastically transformed. These transformations mainly aim to aid a healthcare personnel with diagnosis and prognosis of a disease or abnormality at any given point of healthcare routine workflow. For instance, many of the cancer metastases detection depends on pathological tissue procedures and pathologist reviews. The reports of severity classification vary amongst different pathologist, which then leads to different treatment options for a patient. This labor-intensive work can lead to errors or mistreatments resulting in high cost of healthcare. With the help of machine learning and deep learning modules, some of these traditional diagnosis techniques can be improved and aid a doctor in decision making with an unbiased view. Some of such modules can help reduce the cost, shortage of an expertise, and time in identifying the disease. However, there are many other datapoints that are available with medical images, such as omics data, biomarker calculations, patient demographics and history. All these datapoints can enhance disease classification or prediction of progression with the help of machine learning/deep learning modules. However, it is very difficult to find a comprehensive dataset with all different modalities and features in healthcare setting due to privacy regulations. Hence in this thesis, we explore both medical imaging data with clinical datapoints as well as genomics datasets separately for classification tasks using combinational deep learning architectures. We use deep neural networks with 3D volumetric structural magnetic resonance images of Alzheimer Disease dataset for classification of disease. A separate study is implemented to understand classification based on clinical datapoints achieved by machine learning algorithms. For bioinformatics applications, sequence classification task is a crucial step for many metagenomics applications, however, requires a lot of preprocessing that requires sequence assembly or sequence alignment before making use of raw whole genome sequencing data, hence time consuming especially in bacterial taxonomy classification. There are only a few approaches for sequence classification tasks that mainly involve some convolutions and deep neural network. A novel method is developed using an intrinsic nature of recurrent neural networks for 16s rRNA sequence classification which can be adapted to utilize read sequences directly. For this classification task, the accuracy is improved using optimization techniques with a hybrid neural network