M Dyva Sugnana Rao

@bvrithyderabad.edu.in

Asst.Professor
BVRIT HYDERABAD College of Engineering for Women

M Dyva Sugnana Rao


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https://researchid.co/msugnan78

EDUCATION

B.Tech(CSE),M.Tech(CSE),(PhD)

RESEARCH, TEACHING, or OTHER INTERESTS

Computer Science, Multidisciplinary
3

Scopus Publications

115

Scholar Citations

3

Scholar h-index

2

Scholar i10-index

Scopus Publications

  • A Stable Method For Brain Tumor Prediction In Magnetic Resonance Images Using Fine-tuned XceptionNet
    Shanmuga Sundari.M, Yeluri Divya, KBKS Durga, Vidyullatha Sukhavasi, M.Dyva Sugnana Rao, M.Sudha Rani
    International Journal of Computing and Digital Systems, 2024
    Brain tumors can be a life-threatening condition, and early detection is crucial for effective treatment.Magnetic resonance imaging (MRI) is a valuable appliance for identifying the tumor's location, but manual detection is a time-engrossing and flaws-prone process.To overcome these challenges, computer-assisted approaches have been developed, and deep learning (DL) archetypes are now being pre-owned in medical imaging to discover brain tumors maneuver MRI carbon copies.In this, we propose a deep convolutional neural network (CNN) Xception net model for the efficient classification and detection of brain tumor images.We utilized the "Br35H :: Brain Tumor Detection 2020" dataset sourced from Kaggle, which encompasses 3000 MRI images of brain tumors, each with a file size of 88 megabytes.The Xception net is a powerful CNN model that has shown promising results in various systems perceiving exercise, in conjunction with medical illustration scrutiny.We fine-tuned the Xception net model using a dataset of Magnetic Resonance Imaging (MRI) images of the brain, which were pre-processed and labeled by medical experts.To reckon the performance of our prototype, we counselled dossier using a variety of interpretation criterion, including accuracy, precision, recall, and F1 score.Our customs view that the urged model achieved high accuracy in classifying brain tumor images.The archetype's strength to accurately and efficiently classify and detect brain tumors using MRI images can significantly improve patient outcomes by enabling early detection and treatment.Overall, our study demonstrates the persuasiveness of using the Xception net flawless for brain tumor ferreting out and alloting using MRI images with 94% of accuracy performance.The proposed model has the potential to revolutionize the department of salutary exemplify and improve patient outcomes for brain tumor treatment.
  • Effective Prediction Analysis for Cardiovascular Using Various Machine Learning Algorithms
    M. Shanmuga Sundari, M. Dyva Sugnana Rao, Ch Anil Kumar
    Lecture Notes in Networks and Systems, 2023
  • Covid-19 X-Ray Image Detection using ResNet50 and VGG16 in Convolution Neural Network
    Shanmuga Sundari M, M.Dyva Sugnana Rao, M. Sudha Rani, KBKS Durga, A. Kranthi
    2022 IEEE Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation Iatmsi 2022, 2022
    COVID-19 is an outbreak of disease which is created by China. COVID-19 is originated by coronavirus (CoV), generally created mutation pattern with ‘SARS-CoV2’ or ‘2019 novel coronavirus’. It is declared by the World Health Organization of 2019 in December. COVID-19 is a contagious virus and contiguous disease that will create the morality of life. Even though it is detected in an early stage it can be incurable if the severity is more. The throat and nose samples are collected to identify COVID-19 disease. We collected the X-Ray images to identify the virus. We propose a system to diagnose the images using Convolutional Neural Network (CNN) models. Dataset used consists of both Covid and Normal X-ray images. Among Convolutional Neural Network (CNN) models, the proposed models are ResNet50 and VGG16. RESNET50 consists of 48 convolutional, 1 MaxPool, and Average Pool layers, and VGG16 is another convolutional neural network that consists of 16 deep layers. By using these two models, the detection of COVID-19 is done. This research is designed to help physicians for successful detection of COVID-19 disease at an early stage in the medical field.

RECENT SCHOLAR PUBLICATIONS

  • BLOCKCHAIN BASED E-COMMERCE ONLINE APPLICATION USING DISTRIBUTED LEDGER TECHNOLOGY
    MMSR M Dyva Sugnana Rao,Dr B Venkataramana,Ms Tadi Satya Kumari,Dr anil ...
    IN Patent 37/2,024 , 2024
    2024
  • A stable method for brain tumor prediction in magnetic resonance images using finetuned XceptionNet
    S Sundari, Y Divya, K Durga, V Sukhavasi, MD Sugnana Rao, MS Rani
    University Of Bahrain , 2024
    2024
    Citations: 17
  • A SYSTEM AND APPARATUS FOR ADVANCED ENHANCED NEURAL STYLE TRANSFER (NST) METHOD LEVERAGING RESNET ARCHITECTURE FOR HIGH-FIDELITY IMAGE SYNTHESIS
    MDSR Dr. K.V.N Sunitha , Ms. M. Shanmuga Sundari ,Dr G Naga Satish,Dr R ...
    IN Patent App. 202341066332 A , 2023
    2023
  • House price prediction using machine learning
    A Shahasane, M Gosavi, A Bhagat, N Mishra, A Nerurkar
    International Research Journal of Engineering and Technology (IRJET) 10 (4 … , 2023
    2023
    Citations: 88
  • Effective Prediction Analysis for Cardiovascular Using Various Machine Learning Algorithms
    M Shanmuga Sundari, M Dyva Sugnana Rao, CA Kumar
    Proceedings of Fourth International Conference on Computer and Communication … , 2023
    2023
    Citations: 1
  • COVID-19 X-ray image detection using ResNet50 and VGG16 in convolution neural network
    MDS Rao, MS Rani, K Durga, A Kranthi
    2022 IEEE Conference on Interdisciplinary Approaches in Technology and … , 2022
    2022
    Citations: 9
  • An Enhanced Process of Split Payment Transaction and Management (SPTM) System
    RS M Dyva Sugnana Rao, Murali Nath, L Lakshmi, GN Satish, ...
    IN Patent 2,020,103,398 , 2021
    2021
  • A Survey on Intelligent Transportation Vehicles Analysis and Prediction of Traffic Based on Big Data
    MDSR M Sudha Rani
    Test Engineering and Managment 82 (ISSN:0193), 12412-12417 , 2020
    2020

MOST CITED SCHOLAR PUBLICATIONS

  • House price prediction using machine learning
    A Shahasane, M Gosavi, A Bhagat, N Mishra, A Nerurkar
    International Research Journal of Engineering and Technology (IRJET) 10 (4 … , 2023
    2023
    Citations: 88
  • A stable method for brain tumor prediction in magnetic resonance images using finetuned XceptionNet
    S Sundari, Y Divya, K Durga, V Sukhavasi, MD Sugnana Rao, MS Rani
    University Of Bahrain , 2024
    2024
    Citations: 17
  • COVID-19 X-ray image detection using ResNet50 and VGG16 in convolution neural network
    MDS Rao, MS Rani, K Durga, A Kranthi
    2022 IEEE Conference on Interdisciplinary Approaches in Technology and … , 2022
    2022
    Citations: 9
  • Effective Prediction Analysis for Cardiovascular Using Various Machine Learning Algorithms
    M Shanmuga Sundari, M Dyva Sugnana Rao, CA Kumar
    Proceedings of Fourth International Conference on Computer and Communication … , 2023
    2023
    Citations: 1
  • BLOCKCHAIN BASED E-COMMERCE ONLINE APPLICATION USING DISTRIBUTED LEDGER TECHNOLOGY
    MMSR M Dyva Sugnana Rao,Dr B Venkataramana,Ms Tadi Satya Kumari,Dr anil ...
    IN Patent 37/2,024 , 2024
    2024
  • A SYSTEM AND APPARATUS FOR ADVANCED ENHANCED NEURAL STYLE TRANSFER (NST) METHOD LEVERAGING RESNET ARCHITECTURE FOR HIGH-FIDELITY IMAGE SYNTHESIS
    MDSR Dr. K.V.N Sunitha , Ms. M. Shanmuga Sundari ,Dr G Naga Satish,Dr R ...
    IN Patent App. 202341066332 A , 2023
    2023
  • An Enhanced Process of Split Payment Transaction and Management (SPTM) System
    RS M Dyva Sugnana Rao, Murali Nath, L Lakshmi, GN Satish, ...
    IN Patent 2,020,103,398 , 2021
    2021
  • A Survey on Intelligent Transportation Vehicles Analysis and Prediction of Traffic Based on Big Data
    MDSR M Sudha Rani
    Test Engineering and Managment 82 (ISSN:0193), 12412-12417 , 2020
    2020