Articles

Permanent URI for this collectionhttp://repository.kln.ac.lk/handle/123456789/15894

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Author: search.filters.author.Welhenge, AnuradhiHas files: Yes

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    Fog computing based ultrasound nerve segmentation system using deep learning for mIoT
    (2022) Welhenge, Anuradhi
    Internet of Things is an ever expanding field and applications can be used for medical field. Patient monitoring and diagnosis can be done with the help of IoT and the problems of storing large amount of data can be solved by using cloud computing. However, when transmitting large amount of data through the network, the latency will be impacted. This can be eliminated by introducing a fog layer for the processing of data and processed data later can be stored in the cloud. This study proposes a novel architecture for a hospital ultrasound system and deep learning algorithm is used for the nerve segmentation and a good accuracy is achieved.
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    Deep learning based breast cancer detection system using fog computing
    (Journal of Discrete Mathematical Sciences & Cryptography, 2022) Welhenge, Anuradhi
    Among the different types of cancers, more women are suffering from breast cancer. Breast cancer can be identified by mammograms or using ultrasounds. Early detection of the cancer can be used to minimize the complexities the women will face. Deep learning based techniques such as convolutional neural networks (CNN) are used to detect the cancer from mammograms or ultrasound scans. In this study, VGGNet based CNN is used to detect the cancer cells. A novel architecture for collecting, processing and storing of patient data is proposed in this study involving a fog layer. This study achieved a high accuracy, sensitivity and specificity compared to previous studies.
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    Blood Pressure Estimation from Photoplethysmography with Motion Artifacts using Long Short Term Memory Network
    (Journal of Biomimetics, Biomaterials and Biomedical Engineering (Volume 54), 2022) Welhenge, Anuradhi; Taparugssanagorn, Attaphongse
    Continuous measurement of the Blood Pressure (BP) is important in hypertensive patientsand elderly population. Traditional cuff based methods are difficult to use since it is uncomfortable towear a cuff throughout the day. A more suitable method is to estimate the BP using the Photoplethysmography(PPG) signal. However, it is difficult to estimate a BP when the PPG is corrupted withMotion Artifacts (MAs). In this paper, Long Short Term Memory (LSTM) an extension of RecurrentNeural Networks (RNN) is used used to improve the accuracy of the estimation of the BP from thecorrupted PPG. It shows that an accuracy of 97.86 is achieved.