Faculty Publications

Now showing 1 - 3 of 3

COVID-19: Automatic detection from X-ray images by utilizing deep learning methods
(Elsevier Ltd, 2021) Nigam, B.; Nigam, A.; Jain, R.; Dodia, S.; Arora, N.; Annappa, B.
In recent months, a novel virus named Coronavirus has emerged to become a pandemic. The virus is spreading not only humans, but it is also affecting animals. First ever case of Coronavirus was registered in city of Wuhan, Hubei province of China on 31st of December in 2019. Coronavirus infected patients display very similar symptoms like pneumonia, and it attacks the respiratory organs of the body, causing difficulty in breathing. The disease is diagnosed using a Real-Time Reverse Transcriptase Polymerase Chain reaction (RT-PCR) kit and requires time in the laboratory to confirm the presence of the virus. Due to insufficient availability of the kits, the suspected patients cannot be treated in time, which in turn increases the chance of spreading the disease. To overcome this solution, radiologists observed the changes appearing in the radiological images such as X-ray and CT scans. Using deep learning algorithms, the suspected patients’ X-ray or Computed Tomography (CT) scan can differentiate between the healthy person and the patient affected by Coronavirus. In this paper, popular deep learning architectures are used to develop a Coronavirus diagnostic systems. The architectures used in this paper are VGG16, DenseNet121, Xception, NASNet, and EfficientNet. Multiclass classification is performed in this paper. The classes considered are COVID-19 positive patients, normal patients, and other class. In other class, chest X-ray images of pneumonia, influenza, and other illnesses related to the chest region are included. The accuracies obtained for VGG16, DenseNet121, Xception, NASNet, and EfficientNet are 79.01%, 89.96%, 88.03%, 85.03% and 93.48% respectively. The need for deep learning with radiologic images is necessary for this critical condition as this will provide a second opinion to the radiologists fast and accurately. These deep learning Coronavirus detection systems can also be useful in the regions where expert physicians and well-equipped clinics are not easily accessible. © 2021 Elsevier Ltd
FedCure: A Heterogeneity-Aware Personalized Federated Learning Framework for Intelligent Healthcare Applications in IoMT Environments
(Institute of Electrical and Electronics Engineers Inc., 2024) Sachin, D.N.; Annappa, B.; Hegde, S.; Abhijit, C.S.; Ambesange, S.
The advent of the Internet of Medical Things (IoMT) devices has led to a healthcare revolution, introducing a new era of smart applications driven by Artificial Intelligence (AI). These advanced technologies have greatly influenced the healthcare industry and have played a crucial role in enhancing the quality of life globally. Federated Learning (FL) has become popular as a technique to create models that can be shared universally using the vast datasets collected from IoMT devices while maintaining data privacy. However, the complex variations in IoMT environments, including diverse devices, data characteristics, and model complexities, create challenges for the straightforward application of traditional FL methods. Consequently, it is not well-suited for deployment in such contexts. This paper introduces FedCure, a personalized FL framework tailored for intelligent IoMT-based healthcare applications operating within a cloud-edge architecture. FedCure is adept at addressing the challenges within IoMT environments by employing personalized FL techniques that can effectively mitigate the impact of heterogeneity. Furthermore, the integration of edge computing technology enhances processing speed and minimizes latency in intelligent IoMT applications. Lastly, this research showcases several case studies encompassing IoMT-based applications, such as Eye Retinopathy Detection, Diabetes Monitoring, Maternal Health, Remote Health Monitoring, and Human Activity Recognition. These case studies provide a means to assess the effectiveness of the proposed FedCure framework and showcase exceptional performance with accuracy and minimal communication overhead, especially in addressing the challenges posed by heterogeneity. © 2013 IEEE.
Federated learning for digital healthcare: concepts, applications, frameworks, and challenges
(Springer, 2024) Sachin, D.N.; Annappa, B.; Ambesange, S.
Various hospitals have adopted digital technologies in the healthcare sector for various healthcare-related applications. Due to the effect of the Covid-19 pandemic, digital transformation has taken place in many domains, especially in the healthcare domain; it has streamlined various healthcare activities. With the advancement in technology concept of telemedicine evolved over the years and led to personalized healthcare and drug discovery. The use of machine learning (ML) technique in healthcare enables healthcare professionals to make a more accurate and early diagnosis. Training these ML models requires a massive amount of data, including patients’ personal data, that need to be protected from unethical use. Sharing these data to train ML models may violate data privacy. A distributed ML paradigm called federated learning (FL) has allowed different medical research institutions, hospitals, and healthcare devices to train ML models without sharing raw data. This survey paper overviews existing research work on FL-related use cases and applications. This paper also discusses the state-of-the-art tools and techniques available for FL research, current shortcomings, and future challenges in using FL in healthcare. © The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature 2024.

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