Scopus Index Publications

Now showing 1 - 3 of 3

Embargo
Throat AI - An Intelligent System For Detecting Foreign Objects In Lateral Neck X-Ray Images
(Institute of Electrical and Electronics Engineers Inc., 2025) Baddewithana, P; Krishara, J; Yapa, K
Foreign Object ingestion is a commonly encountered medical condition within the Ear, Nose, and Throat clinical domain. Timely and accurate detection of such objects is vital, as it often guides the need for surgical intervention. Among the available imaging techniques, lateral neck X-rays are the most widely used radiographs to visualize and assess the presence of FOs in the throat. However, manual interpretation of these images can be time-consuming and subject to human error, potentially leading to misdiagnosis or delayed treatment. This research presents a deep learning-based software solution, deployable via web and mobile platforms, aimed at assisting medical professionals with the automated detection of FOs in lateral neck X-rays. The system leverages state-of-the-art YOLO object detection models, specifically evaluating novel versions such as YOLO-NAS-s, YOLOv11s, and YOLOv8s-OBB to ensure high detection accuracy and deployment efficiency. The best-performing model, YOLO-NAS-s, achieved a validation accuracy of 96.3%. For deployment, the model was hosted on the Roboflow platform and accessed via a FastAPI-based middleware server. Performance evaluation showed an average inference time of approximately 2 seconds and a memory footprint of around 100 MB on standard computing hardware, demonstrating its suitability for integration into resource-constrained clinical environments. This setup highlights the system's lightweight design and real-world applicability. Training, evaluation, and testing of the deep learning models were conducted using a dataset curated from public local healthcare institutions and online medical imaging repositories.
Embargo
Blockchain-based Secure Environment for Electronic Health Records
(IEEE, 2022-11-26) Jayasinghe, J. G. L. A; Shiranthaka, K. G. S.; Kavith, T; Jayasinghe, M. H. D. V.; Yapa Abeywardena, K; Yapa, K
Electronic health records (EHRs) have become the de facto standard for storing patient data in hospitals because of the data technology revolution. Many hospitals use server-based systems to keep track of patient medical records, however, this limits the scalability of those systems because they require a lot of storage space. Interoperability and security and privacy concerns, as well as cyber-attacks on the centralized storage, are among the issues they are dealing with. Lab report downloads can be compromised by a poor authentication mechanism that can be easily shared with a third party. Highlighted issues will be addressed by the proposed system, a Blockchain-based private patient information management system. Using a distributed, immutable, and secure ledger, the solution promises efficient system access and retrieval. Consensus can be achieved without consuming a big amount of energy or causing network congestion thanks to an enhanced consensus technique. Because of their tight zero-knowledge requirement, near-perfect data interchange across many platforms is possible thanks to Non-Fungible Tokens, which encourage openness and immutability in the data flow. In addition, the proposed system uses a mix of a hybrid access control system and public key cryptography to ensure high levels of data protection. Additionally, it is a fantastic accomplishment when Lab Report Download Portal and the report generator for medical lab reports can be connected to the main system, which can dynamically modify the report template format with multi-factor authentication enabled. Know your customer verification is also used to authenticate the user to the system. Decentralizing the medical industry’s data storage, sharing, and record-keeping is the general goal of this solution; this method eliminates the need for paper records.
Embargo
A steganography-based fingerprint authentication mechanism to counter fake physical biometrics and trojan horse attacks
(IEEE, 2021-12-06) Karunathilake, H; Shahan, A. R. M; Shamry, M. N. M; De Silva, M. W. D. S; Senarathne, A. N; Yapa, K
In the modern world, unique biometrics of every individual play a vital role in authentication processes. However, as convenient as it seems, biometrics come with their own set of drawbacks. For instance, if a passphrase is compromised (which is highly likely), changing it to a new passphrase would solve the issue. However, when someone's biometrics are compromised, there is no turning back. Simultaneously, biometric systems are often compromised due to the use of fake physical biometrics and trojan horse attacks that are capable of modifying the authentication process to fulfill a malicious user's intents. This research focuses on proposing a novel and secure authentication process that uses steganography. This “all-in-one” solution also focuses on mitigating the aforementioned drawbacks with the use of four modules, namely, the feature extraction module, the payload generation and authentication module, the fake physical biometrics countering module and the trojan horse countering module. This solution is implemented such that the idea behind it can be easily adopted to enhance the existing biometric authentication systems as well as improve the overall condition and user experience of the multi-factor authentication processes that are widely in use today.

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