Zhang, S., Liu, Y., Liao, W., Ron Zee Tan, R., Bi, R., & Olivo, M. (2024). Ex Vivo Tissue Classification Using Broadband Hyperspectral Imaging Endoscopy and Artificial Intelligence: A Pilot Study. IEEE Sensors Journal, 24(15), 24737–24749. https://doi.org/10.1109/jsen.2024.3411165
Abstract:
Endoscopy stands as a prevalent method for cancer detection; however, there are limitations in its diagnostic ability and time efficiency for clinical applications. In this study, we address these limitations by employing hyperspectral imaging (HSI) across visible, near-infrared, and short-wavelength infrared (SWIR) wavelengths. Utilizing machine learning and deep learning frameworks, we develop a diagnostic probe capable of ultrabroadband hyperspectral sensing, subsequently enhancing existing classification techniques. Hyperspectral datacubes from breast tissue samples are captured and serve as the foundation for our models. Machine learning generates optimal models by fitting against multiple possibilities, while deep learning employs an advanced convolutional neural network (CNN). Both methods yield promising real-time classification results, with accuracies reaching up to 90%. While deep learning models exhibit superior accuracy, their training processes require larger datasets and correspondingly lengthened time spans, balancing the overall utility of both frameworks. Our research is a big step forward toward more precise and time-efficient endoscopic cancer sensing and diagnosis.
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Funding Info:
This research / project is supported by the Agency for Science, Technology and Research - Career Development Award
Grant Reference no. : 202D800042
This research / project is supported by the Agency for Science, Technology and Research - Central Research Fund ATR
Grant Reference no. : NA
This research / project is supported by the I&E - ESPL Gap Funding
Grant Reference no. : I22AEAG006
This research / project is supported by the White space - National Semiconductor Translation and Innovation Centre
Grant Reference no. : NA