Browsing by Author "Radha, R.C."

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Design of Pseudo-Linear Feedback Shift Register (LFSR) based physical unclonable function
(Sultan Qaboos University, 2024) Reddy, J.D.; Devarapalli, A.; Radha, R.C.; Vittal, K.P.
Physical Unclonable Functions are used for authenticating hardware devices. This paper discusses implementing a pseudo-linear feedback shift register-based Physical Unclonable Function on an Artix-7 device within the Basys-3 Field Programmable Gate Array development board. The primary goal is to create an area-efficient Physical Unclonable Function that generates more challenge-response pairs than conventional pseudo-linear feedback shift register-based designs. The design relies on a linear feedback shift register but uses combinational circuits such as inverters and XOR gates instead of shift registers. The strength of the Physical Unclonable Function is based on the quantity of challenge-response pairs it generates, with a larger set indicating better security. The proposed design produces a large-bit response within a stipulated area, capturing n bits of response from a single n-bit challenge. Additionally, the mapping of challenge and response pairs can be varied without altering the hardware structure. Typically, Physical Unclonable Functions are implemented on Field Programmable Gate Arrays and Application Specific Integrated Circuits. This study details the design of a robust linear feedback shift register-based Physical Unclonable Function and how the modified design increases challenge-response pairs within a given area on Field Programmable Gate Array fabric. © 2024, Sultan Qaboos University. All rights reserved.
LCSNet: Lightweight Caries Segmentation Network for the segmentation of dental caries using smartphone photographs
(Elsevier B.V., 2025) Radha, R.C.; Raghavendra, B.S.; Hota, R.K.; Vijayalakshmi, K.R.; Patil, S.; Narasimhadhan, A.V.
Dental caries is one of the major dental issues that is common among many individuals. It leads to tooth loss and affects the tooth root, creating a need to automatically detect dental caries to reduce treatment costs and prevent its consequences. The Lightweight Caries Segmentation Network (LCSNet) proposed in this study detects the location of dental caries by applying pixel-wise segmentation to dental photographs taken with various Android phones. LCSNet utilizes a Dual Multiscale Residual (DMR) block in both the encoder and decoder, adapts transfer learning through a pre-trained InceptionV3 model at the bottleneck layer, and incorporates a Squeeze and Excitation block in the skip connection, effectively extracting spatial information even from images where 95 % of the background and only 5 % represent the area of interest. A new dataset was developed by gathering oral photographs of dental caries from two hospitals, with advanced augmentation techniques applied. The LCSNet architecture demonstrated an accuracy of 97.36 %, precision of 73.1 %, recall of 70.2 %, an F1-Score of 71.14 %, and an Intersection-over-Union (IoU) of 56.8 %. Expert dentists confirmed that the LCSNet model proposed in this in vivo study accurately segments the position and texture of dental caries. Both qualitative and quantitative performance analyses, along with comparative analyses of efficiency and computational requirements, were conducted with other deep learning models. The proposed model outperforms existing deep learning models and shows significant potential for integration into a smartphone application-based oral disease detection system, potentially replacing some conventional clinically adapted methods. © 2025 The Authors.
Machine learning techniques for periodontitis and dental caries detection: A narrative review
(Elsevier Ireland Ltd, 2023) Radha, R.C.; Raghavendra, B.S.; Subhash, B.V.; Rajan, J.; Narasimhadhan, A.V.
Objectives: In recent years, periodontitis, and dental caries have become common in humans and need to be diagnosed in the early stage to prevent severe complications and tooth loss. These dental issues are diagnosed by visual inspection, measuring pocket probing depth, and radiographs findings from experienced dentists. Though a glut of machine learning (ML) algorithms has been proposed for the automated detection of periodontitis, and dental caries, determining which ML techniques are suitable for clinical practice remains under debate. This review aims to identify the research challenges by analyzing the limitations of current methods and how to address these to obtain robust systems suitable for clinical use or point-of-care testing. Methods: An extensive search of the literature published from 2015 to 2022 written in English, related to the subject of study was sought by searching the electronic databases: PubMed, Institute of Electrical and Electronics Engineers (IEEE) Xplore, and ScienceDirect. Results: The initial electronic search yielded 1743 titles, and 55 studies were eventually included based on the selection criteria adopted in this review. Studies selected were on ML applications for the automatic detection of periodontitis and dental caries and related dental issues: Apical lessons, Periodontal bone loss, and Vertical root fracture. Conclusion: While most of the ML-based studies use radiograph images for the detection of periodontitis and dental caries, few pieces of the literature revealed that good diagnostic accuracy could be achieved by training the ML model even with mobile photos representing the images of dental issues. Nowadays smartphones are used in every sector for different applications. Training the ML model with as many images of dental issues captured by the smartphone can achieve good accuracy, reduce the cost of clinical diagnosis, and provide user interaction. © 2023 Elsevier B.V.