Dr Manaswini Jena

@bgu.ac.in

Assistant Proffessor , Computer Science and Engineering
Birla Global University

Dr Manaswini Jena


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https://researchid.co/manaswini

EDUCATION

Ph.D. in Computer Science and Engineering

RESEARCH INTERESTS

Machine Learning
Neural Networks
Deep Learning
Image processing
Evolutionary Algorithms
11

Scopus Publications

135

Scholar Citations

6

Scholar h-index

5

Scholar i10-index

Scopus Publications

  • Adopting state transition testing techniques in scenario-based test case generation
    Sonali Pradhan, Mitrabinda Ray, Manaswini Jena, Murchhana Tripathy, Itishree Panda
    International Journal of System Assurance Engineering and Management, 2025
  • Harnessing the Power of AI: A Resource-Based View of the MedTech Industry
    Murchhana Tripathy, Manaswini Jena, Vivek kumar Garg, Yaswanth Srinivas Karri, Debjyoti De
    Studies in Computational Intelligence, 2025
  • Deep Neural Networks Performance Comparison for Handwritten Text Recognition
    Anjani Kumar Singha, Manaswini Jena, Swaleha Zubair, Pradeep Kumar Tiwari, Abhay Pratap Singh Bhadauria
    Lecture Notes in Networks and Systems, 2024
  • A Tailored Complex Medical Decision Analysis Model for Diabetic Retinopathy Classification Based on Optimized Un-Supervised Feature Learning Approach
    Manaswini Jena, Debahuti Mishra, Smita Prava Mishra, Pradeep Kumar Mallick
    Arabian Journal for Science and Engineering, 2023
  • Exploring the Parametric Impact on a Deep Learning Model and Proposal of a 2-Branch CNN for Diabetic Retinopathy Classification with Case Study in IoT-Blockchain based Smart Healthcare System
    Manaswini Jena, Debahuti Mishra, Smita Prava Mishra, Pradeep Kumar Mallick, Sachin Kumar
    Informatica Slovenia, 2022
    Smart healthcare has changed the way how the patient interacts with the specialists for treatment. However, security and support for various diseases are still the concern for such smart automated systems. One of the critical diseases namely Diabetic Retinopathy (DR), is a major concern for the person with prolonged diabetes and may lead to complete blindness irrespective of age groups. Moreover, in recent years blockchain has gained popularity in providing secure communication between sender and receiver. Hence, this work focus on designing a blockchain-based smart healthcare system for the early detection of diabetic retinopathy. However, early detection of DR impose complexities and requires expert diagnosis, which is not available everywhere. Hence, the proposed smart healthcare model contains a Computer-Aided Diagnosis (CAD) assistance for early detection of symptoms of the disease. The CAD model may assist the ophthalmologists in the early detection of DR, which requires intensive research in developing an efficient and accurate model that can operate without human interaction. Convolutional Neural Network (CNN) is one of the learning models in AI that has shown its potential in computer vision applications. However, the performance of CNN depends on several factors like the pooling approach, activation function, learning rate and CNN architecture. This study provides an empirical analysis of these factors to design the best model for early detection of DR. The best model can be used to develop IoT based smart devices to detect DR in diabetic patients. The study also explains the importance of IoT and blockchain-based technology for the development of smart healthcare systems. The values of the parameters and type of hyperparameters chosen from the study is used in a proposed 2-branch CNN model, and the model is validated using the Kaggle fundus image set. Analysis of various parameters and using their best values gives an outstanding performance in the proposed 2-branch CNN model.
  • A fully convolutional neural network for recognition of diabetic retinopathy in fundus images
    Manaswini Jena, Smita P. Mishra, Debahuti Mishra
    Recent Advances in Computer Science and Communications, 2021
    Background: Diabetic retinopathy is one of the complexities of diabetics and a major cause of vision loss worldwide which come into sight due to prolonged diabetes. For the automatic detection of diabetic retinopathy through fundus images several technical approaches have been proposed. The visual information processing by convolutional neural network makes itself more suitable due to its spatial arrangement of units. Convolutional neural networks are at their peak of development and best results can be gained by proper use of the technique. The local connectivity, parameter sharing and pooling of hidden units are advantageous for various predictions. Objective: Objective of this paper is to design a model for classification of diabetic retinopathy. Method: A fully convolutional neural network model is developed to classify the diseased and healthy fundus images. Here, proposed neural network consists of six convolutional layers along with rectified linear unit activations and max pooling layers. The absence of fully connected layer reduces the computational complexity of the model and trains faster as compared to traditional convolutional neural network models. Result and Conclusion: The validation of the proposed model is accomplished by training it with a publicly available High-Resolution Fundus image database. The model is also compared with various existing state-of-the-art methods which show competitive result as compared to these models. A behavioural study of different parameters of the network model is represented. The intelligence of our model lies in its ability to re-tune weight to overcome outliers encountered in future. The proposed model works well with satisfactory performance.
  • Pragmatic Study of CNN Model and Different Parameters Impact on It for the Classification of Diabetic Retinopathy
    Manaswini Jena, Smita Prava Mishra, Debahuti Mishra
    Smart Innovation Systems and Technologies, 2021
  • Empirical analysis of activation functions and pooling layers in CNN for classification of diabetic retinopathy
    Manaswini Jena, Smita Prava Mishra, Debahuti Mishra
    Proceedings 2019 International Conference on Applied Machine Learning Icaml 2019, 2019
    Convolution Neural Network is at its peak of development now-a-days. Objective of this paper is to analyze the behavior of a classification model for automatic identification of diabetic retinopathy. A Convolution Neural Network model having four convolution layer and two fully connected layer is tested by taking four different types of pooling layers with four various activation functions. By using same layer of different kinds, the output of the model is evaluated using different evaluation parameters. Distinct results has been observed according to distinct combination of pooling layer and activation function.
  • Detection of Diabetic Retinopathy Images Using a Fully Convolutional Neural Network
    Manaswini Jena, Smita Prava Mishra, Debahuti Mishra
    Proceedings 2nd International Conference on Data Science and Business Analytics Icdsba 2018, 2018
    The objective of this paper is to develop a model for the classification of diabetic retinopathy, a prime cause for blindness that appears due to prolonged diabetes. A deep learning model based on fully convolutional neural network is developed to classify the disease from fundus image of the patient. Here, proposed neural network consists of only six convolutional layers along rectified linear unit (ReLu) activation and max pooling layer. The model trains faster as compared to traditional convolutional neural network models as the absence of fully connected layer reduces the computational complexity. The validation of the proposed model is carried out by training it with a publicly available High-Resolution Fundus (HRF) image database. The model is also compared with various existing state-of-the-art methods which shows competitive result as compared to these models. The intelligence of our model lies in its ability to re-tune weight to overcome outliers encountered in future. The proposed model works well with an accuracy of 91.66%.
  • Review of neural network techniques in the verge of image processing
    Manaswini Jena, Sashikala Mishra
    Advances in Intelligent Systems and Computing, 2018
  • Biological data analysis using hybrid functional link artificial neural network
    Manaswini Jena, Rasmita Dash, Bijan Bihari Misra
    Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2015

RECENT SCHOLAR PUBLICATIONS

  • Deep neural networks performance comparison for handwritten text recognition
    AK Singha, M Jena, S Zubair, PK Tiwari, APS Bhadauria
    International Conference on Mobile Radio Communications & 5G Networks, 539-553 , 2023
    2023.0
    Citations: 3
  • Correction to: Deep Neural Networks Performance Comparison for Handwritten Text Recognition
    AK Singha, M Jena, S Zubair, PK Tiwari, APS Bhadauria
    International Conference on Mobile Radio Communications & 5G Networks, C1-C1 , 2023
    2023.0
  • A tailored complex medical decision analysis model for diabetic retinopathy classification based on optimized un-supervised feature learning approach
    M Jena, D Mishra, SP Mishra, PK Mallick
    Arabian Journal for Science and Engineering 48 (2), 2087-2099 , 2023
    2023.0
    Citations: 9
  • Exploring the parametric impact on a deep learning model and proposal of a 2-branch CNN for diabetic retinopathy classification with case study in IoT-Blockchain based smart …
    M Jena, D Mishra, SP Mishra, PK Mallick, S Kumar
    Informatica 46 (2) , 2022
    2022.0
    Citations: 20
  • A fully convolutional neural network for recognition of diabetic retinopathy in fundus images
    M Jena, SP Mishra, D Mishra
    Recent Advances in Computer Science and Communications (Formerly: Recent … , 2021
    2021.0
    Citations: 5
  • Pragmatic Study of CNN Model and Different Parameters Impact on It for the Classification of Diabetic Retinopathy
    M Jena, SP Mishra, D Mishra
    Intelligent and Cloud Computing: Proceedings of ICICC 2019, Volume 1, 711-718 , 2020
    2020.0
  • Empirical analysis of activation functions and pooling layers in cnn for classification of diabetic retinopathy
    M Jena, SP Mishra, D Mishra
    2019 International Conference on Applied Machine Learning (ICAML), 34-39 , 2019
    2019.0
    Citations: 13
  • A survey on applications of machine learning techniques for medical image segmentation
    M Jena, SP Mishra, D Mishra
    Internationa Journal of Engineering & Technology 7 (4), 4489-4495 , 2018
    2018.0
    Citations: 52
  • Detection of diabetic retinopathy images using a fully convolutional neural network
    M Jena, SP Mishra, D Mishra
    2018 2nd International Conference on Data Science and Business Analytics … , 2018
    2018.0
    Citations: 18
  • Review of neural network techniques in the verge of image processing
    M Jena, S Mishra
    International Proceedings on Advances in Soft Computing, Intelligent Systems … , 2017
    2017.0
    Citations: 14
  • Biological data analysis using hybrid functional link artificial neural network
    M Jena, R Dash, BB Misra
    International Conference on Swarm, Evolutionary, and Memetic Computing, 88-97 , 2014
    2014.0
    Citations: 1
  • Image, Video Forensics, and Multimedia Content Security
    M Jena, SP Mishra, D Mishra

MOST CITED SCHOLAR PUBLICATIONS

  • A survey on applications of machine learning techniques for medical image segmentation
    M Jena, SP Mishra, D Mishra
    Internationa Journal of Engineering & Technology 7 (4), 4489-4495 , 2018
    2018.0
    Citations: 52
  • Exploring the parametric impact on a deep learning model and proposal of a 2-branch CNN for diabetic retinopathy classification with case study in IoT-Blockchain based smart …
    M Jena, D Mishra, SP Mishra, PK Mallick, S Kumar
    Informatica 46 (2) , 2022
    2022.0
    Citations: 20
  • Detection of diabetic retinopathy images using a fully convolutional neural network
    M Jena, SP Mishra, D Mishra
    2018 2nd International Conference on Data Science and Business Analytics … , 2018
    2018.0
    Citations: 18
  • Review of neural network techniques in the verge of image processing
    M Jena, S Mishra
    International Proceedings on Advances in Soft Computing, Intelligent Systems … , 2017
    2017.0
    Citations: 14
  • Empirical analysis of activation functions and pooling layers in cnn for classification of diabetic retinopathy
    M Jena, SP Mishra, D Mishra
    2019 International Conference on Applied Machine Learning (ICAML), 34-39 , 2019
    2019.0
    Citations: 13
  • A tailored complex medical decision analysis model for diabetic retinopathy classification based on optimized un-supervised feature learning approach
    M Jena, D Mishra, SP Mishra, PK Mallick
    Arabian Journal for Science and Engineering 48 (2), 2087-2099 , 2023
    2023.0
    Citations: 9
  • A fully convolutional neural network for recognition of diabetic retinopathy in fundus images
    M Jena, SP Mishra, D Mishra
    Recent Advances in Computer Science and Communications (Formerly: Recent … , 2021
    2021.0
    Citations: 5
  • Deep neural networks performance comparison for handwritten text recognition
    AK Singha, M Jena, S Zubair, PK Tiwari, APS Bhadauria
    International Conference on Mobile Radio Communications & 5G Networks, 539-553 , 2023
    2023.0
    Citations: 3
  • Biological data analysis using hybrid functional link artificial neural network
    M Jena, R Dash, BB Misra
    International Conference on Swarm, Evolutionary, and Memetic Computing, 88-97 , 2014
    2014.0
    Citations: 1
  • Correction to: Deep Neural Networks Performance Comparison for Handwritten Text Recognition
    AK Singha, M Jena, S Zubair, PK Tiwari, APS Bhadauria
    International Conference on Mobile Radio Communications & 5G Networks, C1-C1 , 2023
    2023.0
  • Pragmatic Study of CNN Model and Different Parameters Impact on It for the Classification of Diabetic Retinopathy
    M Jena, SP Mishra, D Mishra
    Intelligent and Cloud Computing: Proceedings of ICICC 2019, Volume 1, 711-718 , 2020
    2020.0
  • Image, Video Forensics, and Multimedia Content Security
    M Jena, SP Mishra, D Mishra