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Browsing by Author "Gill, Hunter Mathias"
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Item Combining transfer learning with retinal lesion features for accurate detection of diabetic retinopathy(Frontiers Media, 2022-11-08) Hassan, Doaa; Gill, Hunter Mathias; Happe, Michael; Bhatwadekar, Ashay D.; Hajrasouliha, Amir R.; Janga, Sarath Chandra; BioHealth Informatics, School of Informatics and ComputingDiabetic retinopathy (DR) is a late microvascular complication of Diabetes Mellitus (DM) that could lead to permanent blindness in patients, without early detection. Although adequate management of DM via regular eye examination can preserve vision in in 98% of the DR cases, DR screening and diagnoses based on clinical lesion features devised by expert clinicians; are costly, time-consuming and not sufficiently accurate. This raises the requirements for Artificial Intelligent (AI) systems which can accurately detect DR automatically and thus preventing DR before affecting vision. Hence, such systems can help clinician experts in certain cases and aid ophthalmologists in rapid diagnoses. To address such requirements, several approaches have been proposed in the literature that use Machine Learning (ML) and Deep Learning (DL) techniques to develop such systems. However, these approaches ignore the highly valuable clinical lesion features that could contribute significantly to the accurate detection of DR. Therefore, in this study we introduce a framework called DR-detector that employs the Extreme Gradient Boosting (XGBoost) ML model trained via the combination of the features extracted by the pretrained convolutional neural networks commonly known as transfer learning (TL) models and the clinical retinal lesion features for accurate detection of DR. The retinal lesion features are extracted via image segmentation technique using the UNET DL model and captures exudates (EXs), microaneurysms (MAs), and hemorrhages (HEMs) that are relevant lesions for DR detection. The feature combination approach implemented in DR-detector has been applied to two common TL models in the literature namely VGG-16 and ResNet-50. We trained the DR-detector model using a training dataset comprising of 1,840 color fundus images collected from e-ophtha, retinal lesions and APTOS 2019 Kaggle datasets of which 920 images are healthy. To validate the DR-detector model, we test the model on external dataset that consists of 81 healthy images collected from High-Resolution Fundus (HRF) dataset and MESSIDOR-2 datasets and 81 images with DR signs collected from Indian Diabetic Retinopathy Image Dataset (IDRID) dataset annotated for DR by expert. The experimental results show that the DR-detector model achieves a testing accuracy of 100% in detecting DR after training it with the combination of ResNet-50 and lesion features and 99.38% accuracy after training it with the combination of VGG-16 and lesion features. More importantly, the results also show a higher contribution of specific lesion features toward the performance of the DR-detector model. For instance, using only the hemorrhages feature to train the model, our model achieves an accuracy of 99.38 in detecting DR, which is higher than the accuracy when training the model with the combination of all lesion features (89%) and equal to the accuracy when training the model with the combination of all lesions and VGG-16 features together. This highlights the possibility of using only the clinical features, such as lesions that are clinically interpretable, to build the next generation of robust artificial intelligence (AI) systems with great clinical interpretability for DR detection. The code of the DR-detector framework is available on GitHub at https://github.com/Janga-Lab/DR-detector and can be readily employed for detecting DR from retinal image datasets.Item Geographical Landscape and Transmission Dynamics of SARS-CoV-2 Variants Across India: A Longitudinal Perspective(Frontiers Media, 2021-12-17) Jha, Neha; Hall, Dwight; Kanakan, Akshay; Mehta, Priyanka; Maurya, Ranjeet; Mir, Quoseena; Gill, Hunter Mathias; Janga, Sarath Chandra; Pandey, Rajesh; Biomedical Engineering and Informatics, Luddy School of Informatics, Computing, and EngineeringGlobally, SARS-CoV-2 has moved from one tide to another with ebbs in between. Genomic surveillance has greatly aided the detection and tracking of the virus and the identification of the variants of concern (VOC). The knowledge and understanding from genomic surveillance is important for a populous country like India for public health and healthcare officials for advance planning. An integrative analysis of the publicly available datasets in GISAID from India reveals the differential distribution of clades, lineages, gender, and age over a year (Apr 2020–Mar 2021). The significant insights include the early evidence towards B.1.617 and B.1.1.7 lineages in the specific states of India. Pan-India longitudinal data highlighted that B.1.36* was the predominant clade in India until January–February 2021 after which it has gradually been replaced by the B.1.617.1 lineage, from December 2020 onward. Regional analysis of the spread of SARS-CoV-2 indicated that B.1.617.3 was first seen in India in the month of October in the state of Maharashtra, while the now most prevalent strain B.1.617.2 was first seen in Bihar and subsequently spread to the states of Maharashtra, Gujarat, and West Bengal. To enable a real time understanding of the transmission and evolution of the SARS-CoV-2 genomes, we built a transmission map available on https://covid19-indiana.soic.iupui.edu/India/EmergingLineages/April2020/to/March2021. Based on our analysis, the rate estimate for divergence in our dataset was 9.48 e-4 substitutions per site/year for SARS-CoV-2. This would enable pandemic preparedness with the addition of future sequencing data from India available in the public repositories for tracking and monitoring the VOCs and variants of interest (VOI). This would help aid decision making from the public health perspective.Item Mutational Landscape and Interaction of SARS-CoV-2 with Host Cellular Components(MDPI, 2021-09) Srivastava, Mansi; Hall, Dwight; Omoru, Okiemute Beatrice; Gill, Hunter Mathias; Smith, Sarah; Janga, Sarath Chandra; BioHealth Informatics, School of Informatics and ComputingThe emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and its rapid evolution has led to a global health crisis. Increasing mutations across the SARS-CoV-2 genome have severely impacted the development of effective therapeutics and vaccines to combat the virus. However, the new SARS-CoV-2 variants and their evolutionary characteristics are not fully understood. Host cellular components such as the ACE2 receptor, RNA-binding proteins (RBPs), microRNAs, small nuclear RNA (snRNA), 18s rRNA, and the 7SL RNA component of the signal recognition particle (SRP) interact with various structural and non-structural proteins of the SARS-CoV-2. Several of these viral proteins are currently being examined for designing antiviral therapeutics. In this review, we discuss current advances in our understanding of various host cellular components targeted by the virus during SARS-CoV-2 infection. We also summarize the mutations across the SARS-CoV-2 genome that directs the evolution of new viral strains. Considering coronaviruses are rapidly evolving in humans, this enables them to escape therapeutic therapies and vaccine-induced immunity. In order to understand the virus’s evolution, it is essential to study its mutational patterns and their impact on host cellular machinery. Finally, we present a comprehensive survey of currently available databases and tools to study viral–host interactions that stand as crucial resources for developing novel therapeutic strategies for combating SARS-CoV-2 infection.