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30 results on '"Raman, Sundaresan"'

1. Image Processing in Retinal Imaging

Author

Rehana Khan, T. M. A., Bhandari, Vitthal, Raman, Sundaresan, Vyas, Abhishek, Raman, Akshay, Roy, Maitreyee, Raman, Rajiv, Yogesan, Kanagasingam, editor, Goldschmidt, Leonard, editor, Cuadros, Jorge, editor, and Ricur, Giselle, editor

Published
2023
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2. Benchmarking State-of-the-Art Methodologies for Optic Disc Segmentation

Author

Kumar, Subham, Raman, Sundaresan, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Thakur, Manoj, editor, Agnihotri, Samar, editor, Rajpurohit, Bharat Singh, editor, Pant, Millie, editor, Deep, Kusum, editor, and Nagar, Atulya K., editor

Published
2023
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6. Population-Based Artificial Intelligence Assessment of Relationship Between the Risk Factors for Diabetic Retinopathy in Indian Population.

Author

Vyas, Abhishek, Deshpande, Aadit, Sen, Sagnik, Kim, Ramasamy, Rajalakshmi, Ramachandran, Mohan, Viswanathan, Raman, Rajiv, and Raman, Sundaresan

Subjects
GLYCOSYLATED hemoglobin, SYSTOLIC blood pressure, DIABETIC retinopathy, BODY mass index, SUPPORT vector machines
Abstract

Purpose: Risk factors (RFs), like 'body mass index (BMI),' 'age,' and 'gender' correlate with Diabetic Retinopathy (DR) diagnosis and have been widely studied. This study examines how these three secondary RFs independently affect the predictive capacity of primary RFs. Methods: The dataset consisted of four population-based studies on the prevalence of DR and associated RFs in India between 2001 and 2010. An Autoencoder was employed to categorize RFs as primary or secondary. This study evaluated six primary RFs coupled independently with each secondary RF on five machine-learning models. Results: The secondary RF 'gender' gave a maximum increase in Area under the curve (AUC) score to predict DR when combined separately with 'insulin treatment,' 'fasting plasma glucose,' 'hypertension history,' and 'glycosylated hemoglobin' with a maximum increase in AUC for the Naive Bayes model from 0.573 to 0.646, for the Support Vector Machines (SVM) model from 0.644 to 0.691, for the SVM model from 0.487 to 0.607, and for the Decision Tree model from 0.8 to 0.848, respectively. The secondary RFs 'age' and 'BMI' gave a maximum increase in AUC score to predict DR when combined separately with 'diabetes mellitus duration' and 'systolic blood pressure,' with a maximum increase in AUC for the SVM model from 0.389 to 0.621, and for the Decision Tree model from 0.617 to 0.713, respectively. Conclusion: The risk factor 'gender' was the best secondary RF in predicting DR compared to 'age' and 'BMI,' increasing the predictive power of four primary RFs. [ABSTRACT FROM AUTHOR]

Published
2024
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8. Lung Nodule Segmentation Using 3-Dimensional Convolutional Neural Networks

Author

Kumar, Subham, Raman, Sundaresan, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Das, Kedar Nath, editor, Bansal, Jagdish Chand, editor, Deep, Kusum, editor, Nagar, Atulya K., editor, Pathipooranam, Ponnambalam, editor, and Naidu, Rani Chinnappa, editor

Published
2020
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10. Analysis of Transfer and Residual Learning for Detecting Plant Diseases Using Images of Leaves

Author

Khandelwal, Ines, Raman, Sundaresan, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Verma, Nishchal K., editor, and Ghosh, A. K., editor

Published
2019
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14. Advances in the diagnosis of herpes simplex stromal necrotising keratitis: A feasibility study on deep learning approach

Author

Natarajan, Radhika, Matai, Hiren, Raman, Sundaresan, Kumar, Subham, Ravichandran, Swetha, Swaminathan, Samyuktha, and Alex, John Rani

Subjects
Keratitis -- Diagnosis, Machine learning -- Analysis, Algorithms -- Analysis, Algorithm, Health
Abstract

Byline: Radhika. Natarajan, Hiren. Matai, Sundaresan. Raman, Subham. Kumar, Swetha. Ravichandran, Samyuktha. Swaminathan, John. Rani Alex Purpose: Infectious keratitis, especially viral keratitis (VK), in resource-limited settings, can be a challenge [...]

Published
2022

27. Canonical and atypical E2Fs regulate the mammalian endocycle

Author

Chen, Hui-Zi, primary, Ouseph, Madhu M., additional, Li, Jing, additional, Pécot, Thierry, additional, Chokshi, Veda, additional, Kent, Lindsey, additional, Bae, Sooin, additional, Byrne, Morgan, additional, Duran, Camille, additional, Comstock, Grant, additional, Trikha, Prashant, additional, Mair, Markus, additional, Senapati, Shantibhusan, additional, Martin, Chelsea K., additional, Gandhi, Sagar, additional, Wilson, Nicholas, additional, Liu, Bin, additional, Huang, Yi-Wen, additional, Thompson, John C., additional, Raman, Sundaresan, additional, Singh, Shantanu, additional, Leone, Marcelo, additional, Machiraju, Raghu, additional, Huang, Kun, additional, Mo, Xiaokui, additional, Fernandez, Soledad, additional, Kalaszczynska, Ilona, additional, Wolgemuth, Debra J., additional, Sicinski, Piotr, additional, Huang, Tim, additional, Jin, Victor, additional, and Leone, Gustavo, additional

Published
2012
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30. Phenotypical Analysis of Tumor Microenvironment

Author

Raman, Sundaresan

Subjects
Biomedical Research, Computer Engineering, Computer Science, Tumor Microenvironment, Phenotype, Morphology, Texture, Visualization, Reconstruction, Isosurface
Abstract

Establishing dependency between the genotypes and the phenotypes in any model system is not straightforward especially for complex processes such as cancer. This is primarily due to the fact that the phenotype domain is inconceivably large owing to phenotypes occuring at multiple hierarchies. Although the entire genome sequences of many model systems are now available, the corresponding growth in the phenotype domain lags far behind. This lead to considerable growth in phenotyping technologies in the recent past across multiple hierarchies.In this dissertation, I present a computational framework for quantitative phenotypical analysis primarily directed at cellular scale. It has been recently known in cancer biology that both the tumor cells and the neighboring normal cells collectively constituting the tumor microenvironment, work together in the initiation and progression of tumors. I describe how changes in the genetic make up of those neighboring normal cells of a specific type - fibroblasts, affect their physical features like nuclear morphology. Although the significance of nuclear morphology on tumor progression is well known, there is a strong need to incorporate other phenotypes like nuclear appearance and spatial context especially in the case of gene knockouts like P53 that plays a crucial role in DNA repair. I show how these phenotypes are computed and are affected to different degrees based on specific gene knockouts. Thus the proposed framework could be used as a hypothesis generation tool. Besides characterizing sub-cellular features, I propose an algorithm to reconstruct larger structures like blood vessels and epithelial ducts based on a model-based approach that uses a tracking paradigm to perform the reconstruction. Finally, I show ways to visualize both the tumor microenvironment as well as the computed features using volume and information visualization techniques, and propose an algorithmic technique to reconstruct isosurfaces by avoiding smaller area triangles and edges by extending the existing Marching Cubes lookup table.

Published
2012

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