sudha chekuri - Academia.edu (original) (raw)

Papers by sudha chekuri

Research paper thumbnail of Enhanced Hybrid Privacy Preserving Data Mining Technique

IntechOpen eBooks, Nov 27, 2022

At present, almost every domain is handling large volumes of data even as storage device capaciti... more At present, almost every domain is handling large volumes of data even as storage device capacities increase. Amidst humongous data volumes, Data mining applications help find useful patterns that can be used to drive business growth, improved services, better health care facilities etc. The accumulated data can be exploted for identity theft, fake credit/debit card transactions, etc. In such scenarios, data mining techniques that provide privacy are helpful. Though privacy-preserving data mining techniques like randomization, perturbation, anonymization etc., provide privacy, but when applied separately, they fail to be effective. Hence, this chapter suggests an Enhanced Hybrid Privacy Preserving Data Mining (EHPPDM) technique by combining them. The proposed technique provides more privacy of data than existing techniques while providing better classification accuracy as well as evidenced by our experimental results.

Research paper thumbnail of A Novel Architecture for Feature Extraction and Convolution for Image Segmentation of Pathology Detection from Chest X-Ray Images

Traitement Du Signal, Dec 31, 2022

Neural networks are widely used for the automation of analysis and classification tasks in the fi... more Neural networks are widely used for the automation of analysis and classification tasks in the field of medical image processing. They have successfully achieved state of the art performance in medical image segmentation and feature extraction techniques. This automatic classification in the medical field is very helpful in developing tools for early detection of dreadful pathologies, like tuberculosis and pneumonia, in areas where access to doctors or radiologists is scarce. In this work, we propose a novel approach for the classification of lung pathologies like tuberculosis and pneumonia by masking them in boundary boxes using convolutional neural networks. Our solution provides a flexible way, by using saved trained models that could be directly employed by the Radiologists. In this paper, we describe the architecture required to achieve such a scalable model which could be used by doctors and radiologists without too much training in the technologies of the times. The proposed convolutional architecture consists of connected components which are parallel residual blocks and sampling layers. The images do not lose their original quality, giving the best error free predictions. We visualize this model to be deployed in labs, providing access to medical imaging expertise to some of the most remote places in the world.

Research paper thumbnail of A Novel Architecture for Feature Extraction and Convolution for Image Segmentation of Pathology Detection from Chest X-Ray Images

Traitement du Signal

Neural networks are widely used for the automation of analysis and classification tasks in the fi... more Neural networks are widely used for the automation of analysis and classification tasks in the field of medical image processing. They have successfully achieved state of the art performance in medical image segmentation and feature extraction techniques. This automatic classification in the medical field is very helpful in developing tools for early detection of dreadful pathologies, like tuberculosis and pneumonia, in areas where access to doctors or radiologists is scarce. In this work, we propose a novel approach for the classification of lung pathologies like tuberculosis and pneumonia by masking them in boundary boxes using convolutional neural networks. Our solution provides a flexible way, by using saved trained models that could be directly employed by the Radiologists. In this paper, we describe the architecture required to achieve such a scalable model which could be used by doctors and radiologists without too much training in the technologies of the times. The proposed ...

Research paper thumbnail of Enhanced Hybrid Privacy Preserving Data Mining Technique

Information Security and Privacy in the Digital World - Some Selected Topics [Working Title]

At present, almost every domain is handling large volumes of data even as storage device capaciti... more At present, almost every domain is handling large volumes of data even as storage device capacities increase. Amidst humongous data volumes, Data mining applications help find useful patterns that can be used to drive business growth, improved services, better health care facilities etc. The accumulated data can be exploted for identity theft, fake credit/debit card transactions, etc. In such scenarios, data mining techniques that provide privacy are helpful. Though privacy-preserving data mining techniques like randomization, perturbation, anonymization etc., provide privacy, but when applied separately, they fail to be effective. Hence, this chapter suggests an Enhanced Hybrid Privacy Preserving Data Mining (EHPPDM) technique by combining them. The proposed technique provides more privacy of data than existing techniques while providing better classification accuracy as well as evidenced by our experimental results.

Research paper thumbnail of Enhanced Hybrid Privacy Preserving Data Mining Technique

IntechOpen eBooks, Nov 27, 2022

At present, almost every domain is handling large volumes of data even as storage device capaciti... more At present, almost every domain is handling large volumes of data even as storage device capacities increase. Amidst humongous data volumes, Data mining applications help find useful patterns that can be used to drive business growth, improved services, better health care facilities etc. The accumulated data can be exploted for identity theft, fake credit/debit card transactions, etc. In such scenarios, data mining techniques that provide privacy are helpful. Though privacy-preserving data mining techniques like randomization, perturbation, anonymization etc., provide privacy, but when applied separately, they fail to be effective. Hence, this chapter suggests an Enhanced Hybrid Privacy Preserving Data Mining (EHPPDM) technique by combining them. The proposed technique provides more privacy of data than existing techniques while providing better classification accuracy as well as evidenced by our experimental results.

Research paper thumbnail of A Novel Architecture for Feature Extraction and Convolution for Image Segmentation of Pathology Detection from Chest X-Ray Images

Traitement Du Signal, Dec 31, 2022

Neural networks are widely used for the automation of analysis and classification tasks in the fi... more Neural networks are widely used for the automation of analysis and classification tasks in the field of medical image processing. They have successfully achieved state of the art performance in medical image segmentation and feature extraction techniques. This automatic classification in the medical field is very helpful in developing tools for early detection of dreadful pathologies, like tuberculosis and pneumonia, in areas where access to doctors or radiologists is scarce. In this work, we propose a novel approach for the classification of lung pathologies like tuberculosis and pneumonia by masking them in boundary boxes using convolutional neural networks. Our solution provides a flexible way, by using saved trained models that could be directly employed by the Radiologists. In this paper, we describe the architecture required to achieve such a scalable model which could be used by doctors and radiologists without too much training in the technologies of the times. The proposed convolutional architecture consists of connected components which are parallel residual blocks and sampling layers. The images do not lose their original quality, giving the best error free predictions. We visualize this model to be deployed in labs, providing access to medical imaging expertise to some of the most remote places in the world.

Research paper thumbnail of A Novel Architecture for Feature Extraction and Convolution for Image Segmentation of Pathology Detection from Chest X-Ray Images

Traitement du Signal

Neural networks are widely used for the automation of analysis and classification tasks in the fi... more Neural networks are widely used for the automation of analysis and classification tasks in the field of medical image processing. They have successfully achieved state of the art performance in medical image segmentation and feature extraction techniques. This automatic classification in the medical field is very helpful in developing tools for early detection of dreadful pathologies, like tuberculosis and pneumonia, in areas where access to doctors or radiologists is scarce. In this work, we propose a novel approach for the classification of lung pathologies like tuberculosis and pneumonia by masking them in boundary boxes using convolutional neural networks. Our solution provides a flexible way, by using saved trained models that could be directly employed by the Radiologists. In this paper, we describe the architecture required to achieve such a scalable model which could be used by doctors and radiologists without too much training in the technologies of the times. The proposed ...

Research paper thumbnail of Enhanced Hybrid Privacy Preserving Data Mining Technique

Information Security and Privacy in the Digital World - Some Selected Topics [Working Title]

At present, almost every domain is handling large volumes of data even as storage device capaciti... more At present, almost every domain is handling large volumes of data even as storage device capacities increase. Amidst humongous data volumes, Data mining applications help find useful patterns that can be used to drive business growth, improved services, better health care facilities etc. The accumulated data can be exploted for identity theft, fake credit/debit card transactions, etc. In such scenarios, data mining techniques that provide privacy are helpful. Though privacy-preserving data mining techniques like randomization, perturbation, anonymization etc., provide privacy, but when applied separately, they fail to be effective. Hence, this chapter suggests an Enhanced Hybrid Privacy Preserving Data Mining (EHPPDM) technique by combining them. The proposed technique provides more privacy of data than existing techniques while providing better classification accuracy as well as evidenced by our experimental results.