Conference Papers

Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506

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    Non-invasive blood components measurement using optical sensor system interface
    (Institute of Electrical and Electronics Engineers Inc., 2018) Raikham, P.; Kumar, R.; Shah, R.K.; Hazarika, M.; Sonkar, R.K.
    In this paper, a non-invasive technique to measure glucose, hemoglobin, SpO2 and heart beat using both IR and Red LEDs is presented. The device is made to simultaneously measure the blood components in real time. A comparison with invasive measurements is also shown. © 2018 IEEE.
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    Non-Invasive Detection of Anemia Using Deep Learning on Conjunctival Images
    (Institute of Electrical and Electronics Engineers Inc., 2025) Kedar, D.S.; Pandey, G.; Koolagudi, S.G.
    Anemia, characterized by low levels of red blood cells or hemoglobin, affects millions worldwide, significantly affecting public health. Traditional diagnostic methods, while effective, are invasive, costly, and inaccessible in resource-constrained settings. This paper proposes a non-invasive approach for anemia detection using conjunctival images analyzed through deep learning techniques. The proposed methodology involves capturing high-resolution conjunctival images, pre-processing them, and using a customized Convolutional Neural Network (CNN) for feature extraction and classification. The results achieved by the customized CNN fine-tuned with a batch size of 16 give an Accuracy of 96%, Precision of 95%, Recall of 96%, and ROC-AUC score of 0.99. The customized CNN outperformed the other models for this work, such as Random Forest, XGBoost, SVM, ResNet50, and MobileNetV2. This work highlights the potential for non-invasive diagnostic tools to improve accessibility and efficiency in healthcare, particularly for underserved populations. The findings endorse integrating deep learning in healthcare as a transformative approach to address global challenges such as anemia. © 2025 IEEE.