Dr Manaswini Jena
@bgu.ac.in
Assistant Proffessor , Computer Science and Engineering
Birla Global University
EDUCATION
Ph.D. in Computer Science and Engineering
RESEARCH INTERESTS
Machine Learning
Neural Networks
Deep Learning
Image processing
Evolutionary Algorithms
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Manaswini Jena, Debahuti Mishra, Smita Prava Mishra, and Pradeep Kumar Mallick
Springer Science and Business Media LLC
Manaswini Jena, Debahuti Mishra, Smita Prava Mishra, Pradeep Kumar Mallick, and Sachin Kumar
Slovenian Association Informatika
Manaswini Jena, Smita P. Mishra, and Debahuti Mishra
Bentham Science Publishers Ltd.
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.
Manaswini Jena, Smita Prava Mishra, and Debahuti Mishra
Springer Singapore
Manaswini Jena, Smita Prava Mishra, and Debahuti Mishra
IEEE
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.
Manaswini Jena, Smita Prava Mishra, and Debahuti Mishra
IEEE
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%.
Manaswini Jena and Sashikala Mishra
Springer Singapore
Manaswini Jena, Rasmita Dash, and Bijan Bihari Misra
Springer International Publishing