Browsing by Author "Gopi, Varun P."

Now showing 1 - 7 of 7
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    Automated Binary and Multiclass Classification of Diabetic Retinopathy Using Haralick and Multiresolution Features
    (Institute of Electrical and Electronics Engineers Inc., 2020-03-10) Gayathri, S; Krishna, Adithya K.; Gopi, Varun P.; Palanisamy, P
    Diabetic Retinopathy (DR) is considered as the complication of Diabetes Mellitus that damages the blood vessels in the retina. This is characterized as a serious vision-threatening problem in most of the diabetic subjects. Effective automatic classification of diabetic retinopathy is a challenging task in the medical field. The feature extraction plays an eminent role in the effective classification of disease. The proposed work focuses on the extraction of Haralick and Anisotropic Dual-Tree Complex Wavelet Transform (ADTCWT) features that can perform reliable DR classification from retinal fundus images. The Haralick features are based on second-order statistics and ADTCWT reliably extracts the directional features in images. The proposed work concentrates on both binary classification as well as multiclass classification of DR. The system is evaluated across various classifiers such as Support Vector Machine (SVM), Random Forest, Random Tree, J48 classifiers by giving input image features extracted from the MESSIDOR, KAGGLE and DIARETDB0 databases. The performances of the classifiers are analyzed by comparing specificity, precision, recall, False Positive Rate (FPR) and accuracy values for each classifier. The evaluation results show that by applying the proposed feature extraction method, Random Forest outperforms all the other classifiers with an average accuracy of 99.7% and 99.82% for binary and multiclass classification respectively.
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    Magnitude comparator realization using threshold logic
    (Blue Eyes Intelligence Engineering and Sciences Publication, 2019-09) Soumya, R; Sureh Babu, V; Gopi, Varun P.
    Threshold logic design can be regarded as an alternative method of traditional logic gate design. Threshold logic can be considered as the primary logic of brain. Neurons can be considered as threshold logic gates. Each neuron fires when its activation threshold is crossed. The basic idea behind threshold logic is threshold decision principle. Threshold decision principle checks whether the weighted sum of inputs crosses a threshold and accordingly output is defined. Several implementations for threshold logic were proposed. Resistive threshold logic is a new method for threshold logic implementation. In this paper, using the concept of resistive threshold logic, a magnitude comparator is realized. LTSpiceIV and Electric VLSI Design System are the simulation tools used in this work.
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    Multiresolution cascaded attention U-Net for localization and segmentation of optic disc and fovea in fundus images
    (Nature Research, 2024-10-04) Shalini, R; Gopi, Varun P.
    Identification of retinal diseases in automated screening methods, such as those used in clinical settings or computer-aided diagnosis, usually depends on the localization and segmentation of the Optic Disc (OD) and fovea. However, this task is difficult since these anatomical features have irregular spatial, texture, and shape characteristics, limited sample sizes, and domain shifts due to different data distributions across datasets. This study proposes a novel Multiresolution Cascaded Attention U-Net (MCAU-Net) model that addresses these problems by optimally balancing receptive field size and computational efficiency. The MCAU-Net utilizes two skip connections to accurately localize and segment the OD and fovea in fundus images. We incorporated a Multiresolution Wavelet Pooling Module (MWPM) into the CNN at each stage of U-Net input to compensate for spatial information loss. Additionally, we integrated a cascaded connection of the spatial and channel attentions as a skip connection in MCAU-Net to concentrate precisely on the target object and improve model convergence for segmenting and localizing OD and fovea centers. The proposed model has a low parameter count of 0.8 million, improving computational efficiency and reducing the risk of overfitting. For OD segmentation, the MCAU-Net achieves high IoU values of 0.9771, 0.945, and 0.946 for the DRISHTI-GS, DRIONS-DB, and IDRiD datasets, respectively, outperforming previous results for all three datasets. For the IDRiD dataset, the MCAU-Net locates the OD center with an Euclidean Distance (ED) of 16.90 pixels and the fovea center with an ED of 33.45 pixels, demonstrating its effectiveness in overcoming the common limitations of state-of-the-art methods.
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    Prediction of Gate In Time of Scheduled Flights and Schedule Conformance using Machine Learning-based Algorithms
    (Embry-Riddle Aeronautical University, 2020-11-18) Sahadevan, Deepudev; Palanisamy, P; Gopi, Varun P.; Nelli, Manjunath K.; Asok kumar, A.K.
    Over the past several years, the air transportation system has encountered frequent rises in air traffic demand, especially with the introduction of budget airlines. In recent Indian-aviation submits, IATA has presented that by 2026 India is expected to be the third-largest air transport market in the world (International Air Transport Association, 2018) from its current 7th place. Before pandemic COVID-19, increases in air traffic in trend were predicted such that the current air traffic system will not be able to cater the projected air traffic demand of the near future due to the capacity constraints of airport and airspace. In order to overcome this backdrop, there arises a need for the of modernising the air transportation system. Prompt initiatives are being taken by several countries, including India, to develop the future air transport system that will be more robust, predictable, and reliable than today’s one. Introduction of Central Air Traffic flow management(C-ATFM) in India was one of the steps taken towards achieving this objective.
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    Sub-watershed prioritization using morphometric analysis, principal component analysis, hypsometric analysis, land use/land cover analysis, and machine learning approaches in the Peddavagu River Basin, India
    (IWA Publishing, 2023-06-05) Shekar, Padala Raja; Mathew, Aneesh; Arun, P. S.; Gopi, Varun P.
    Water resource management is critical in the face of climate change to reduce water scarcity and meet the demands of an expanding population. Prioritization of watersheds has gained significance in natural resource management, particularly in the context of watershed management. This study prioritizes sub-watersheds for the Peddavagu basin using five methods. The four methods mentioned above can be estimated utilizing remote sensing (RS) and geographic information system (GIS) approaches, while linear discriminant analysis (LDA) is estimated using machine learning techniques. The catchment resulted in the formation of 13 sub-watersheds. The quantitative measurements of morphometric analysis, including linear, relief, and areal, were considered, and 18 morphometric characteristics were chosen to rank and prioritize sub-watersheds. Principal component analysis (PCA) was used to rank and prioritize sub-watersheds based on four highly correlated morphometric parameters. The land use/land cover (LULC) analysis used four features to prioritize sub-watersheds. The LDA analysis used two features to prioritize sub-watersheds. Using hypsometric integral (HI) values, prioritization has been done. Sub-watersheds were prioritized. Based on five methods, the sub-watersheds were classified as low, medium, and high. Among the sub-watersheds identified as high priority, immediate priority is assigned to SW10. Decision-makers in the research region can use the findings to plan and implement watershed management techniques.
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    Sub-watershed prioritization using morphometric analysis, principal component analysis, hypsometric analysis, land use/land cover analysis, and machine learning approaches in the Peddavagu River Basin, India
    (IWA Publishing, 2023-05-24) Shekar, Padala Raja; Mathew, Aneesh; Arun,P.S.; Gopi, Varun P.
    Water resource management is critical in the face of climate change to reduce water scarcity and meet the demands of an expanding population. Prioritization of watersheds has gained significance in natural resource management, particularly in the context of watershed management. This study prioritizes sub-watersheds for the Peddavagu basin using five methods. The four methods mentioned above can be estimated utilizing remote sensing (RS) and geographic information system (GIS) approaches, while linear discriminant analysis (LDA) is estimated using machine learning techniques. The catchment resulted in the formation of 13 sub-watersheds. The quantitative measurements of morphometric analysis, including linear, relief, and areal, were considered, and 18 morphometric characteristics were chosen to rank and prioritize sub-watersheds. Principal component analysis (PCA) was used to rank and prioritize sub-watersheds based on four highly correlated morphometric parameters. The land use/land cover (LULC) analysis used four features to prioritize sub-watersheds. The LDA analysis used two features to prioritize sub-watersheds. Using hypsometric integral (HI) values, prioritization has been done. Sub-water-sheds were prioritized. Based on five methods, the sub-watersheds were classified as low, medium, and high. Among the sub-watersheds identified as high priority, immediate priority is assigned to SW10. Decision-makers in the research region can use the findings to plan and implement watershed management techniques.
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    VasFAZ-Net: A Hybrid Loss-Optimized E cientNet-scSE Network for Retinal Vasculature and FAZ Segmentation in OCTA
    (Institute of Mathematics and Computer Science, 2026-05-01) Raja, Nisan Pranavah; Gopi, Varun P.; Francis, Bibin; Devi, Chelli N.
    Accurate segmentation of ne retinal vessels and the Foveal Avascular Zone (FAZ) in OCTA images remains challenging due to low contrast, noise, and class imbalance. To address this, a lightweight deep learning framework called VasFAZ-Net is proposed, which uses an E cientNet-B0 encoder, a parallel spatial and channel squeeze-and-excitation (scSE) decoder, and a hybrid Dice-Focal loss function, trained on the OCTA-500 dataset with both 3 mm and 6 mm scans. The proposed model achieved Dice scores of up to 88.48% for vessel segmentation and 97.98% for FAZ segmentation with only 8.8 million parameters and an inference time of 18.2 ms per image. These results demonstrate that the proposed method provides accurate, computationally e cient, and clinically deployable multi-target OCTA segmentation for retinal disease analysis.