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9 results on '"Laishram, Anuradha"'

3. A Deep Learning Approach based on Faster R-CNN for Automatic Detection and Classification of Teeth in Orthopantomogram Radiography Images.

Author

Laishram, Anuradha and Thongam, Khelchandra

Subjects
*DEEP learning, *CONVOLUTIONAL neural networks, *DENTAL radiography, *AUTOMATIC classification, *RADIOGRAPHY, *ANOMALY detection (Computer security)
Abstract

This paper presents a new method for teeth and anomalies detection and classification using Faster Region Convolutional Neural Network with Deep Learning. Four classes of teeth and two classes of teeth anomalies are used for the classification by using Orthopantomogram radiography images as input. Using the Regional Proposal Network (RPN) and Object Detection Network (ODN), the detection of teeth objects has been made possible which replaces the manual segmentation of each individual tooth from the set of teeth signals thereby making the whole system more efficient. Overfitting is avoided by using the Dropout technique and thus improves the accuracy of the system. The model is trained and tested with the input samples and also compared with the ground truth and it achieves an accuracy of 92% for detection and 99.72% for classification. [ABSTRACT FROM AUTHOR]

Published
2024
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6. Automatic Classification of Oral Pathologies Using Orthopantomogram Radiography Images Based on Convolutional Neural Network

Author

Laishram, Anuradha, Thongam, Khelchandra, Laishram, Anuradha, and Thongam, Khelchandra

Abstract

An attempt has been made to device a robust method to classify different oral pathologies using Orthopantomogram (OPG) images based on Convolutional Neural Network (CNN). This system will provide a novel approach for the classification of types of teeth (viz., incisors and molar teeth) and also some underlying oral anomalies such as fixed partial denture (cap) and impacted teeth. To this end, various image preprocessing techniques are performed. The input OPG images are resized, pixels are scaled and erroneous data are excluded. The proposed algorithm is implemented using CNN with Dropout and the fully connected layer has been trained using hybrid GA-BP learning. Using the Dropout regularization technique, over fitting has been avoided and thereby making the network to correctly classify the objects. The CNN has been implemented with different convolutional layers and the highest accuracy of 97.92% has been obtained with two convolutional layers.

Published
2022

9. Phytochemical analysis, TLC profiling and antimicrobial activity of Tephrosia purpurea.

Author

Laishram, Anuradha, Naik, Jayanna, Reddy, Sairamalinga, and Jayasimha Rayalu, D.

Subjects
*PHYTOCHEMICALS, *ANTI-infective agents, *PLANT extracts, *HEXANE, *ETHYL acetate, *METHANOL, *ETHANOL, *TEPHROSIA
Abstract

In the present study we carried out phytochemical analysis and anti microbial activity were performed on different solvent extractions like Hexane, ethyl acetate, methanol, ethanol and aqueous extract of Tephrosia purpurea. Among the all salvent extracts methanol, ethanolic and aqueous extracts have high phytochemicals, based on phytochemical and TLC profiles anti microbial activates was performed with some gram positive ,gram negative and few fungal strains in methanol, ethanolic and aqueous extracts. Based on increased concentration methanol extract shown potent activity against all strains. [ABSTRACT FROM AUTHOR]

Published
2013

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