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Author: search.filters.author.Suvarnaphaet, PhitsiniHas files: No

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    Measurement precision enhancement of surface plasmon resonance based angular scanning detection using deep learning
    (2022-12-01)
    Thadson, Kitsada
    ;
    Sasivimolkul, Suvicha
    ;
    Suvarnaphaet, Phitsini
    ;
    Visitsattapongse, Sarinporn
    ;
    Pechprasarn, Suejit
    Angular scanning-based surface plasmon resonance measurement has been utilized in label-free sensing applications. However, the measurement accuracy and precision of the surface plasmon resonance measurements rely on an accurate measurement of the plasmonic angle. Several methods have been proposed and reported in the literature to measure the plasmonic angle, including polynomial curve fitting, image processing, and image averaging. For intensity detection, the precision limit of the SPR is around 10<sup>–5</sup> RIU to 10<sup>–6</sup> RIU. Here, we propose a deep learning-based method to locate the plasmonic angle to enhance plasmonic angle detection without needing sophisticated post-processing, optical instrumentation, and polynomial curve fitting methods. The proposed deep learning has been developed based on a simple convolutional neural network architecture and trained using simulated reflectance spectra with shot noise and speckle noise added to generalize the training dataset. The proposed network has been validated in an experimental setup measuring air and nitrogen gas refractive indices at different concentrations. The measurement precision recovered from the experimental reflectance images is 4.23 × 10<sup>–6</sup> RIU for the proposed artificial intelligence-based method compared to 7.03 × 10<sup>–6</sup> RIU for the cubic polynomial curve fitting and 5.59 × 10<sup>–6</sup> RIU for 2-dimensional contour fitting using Horner's method.
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    Automated Screening of Cervical Cancer Cell Images
    (2019-01-10)
    Sangworasil, Manas
    ;
    Sukkasem, Chayanisa
    ;
    Sasivimolkul, Suvicha
    ;
    Suvarnaphaet, Phitsini
    ;
    Pechprasarn, Suejit
    Cervical cancer is the first-most common type of female cancer and the second leading cause of death in Thailand. The number of cervical cancer is increasing in every year, even though it is preventable by the screening in early detection. The most popular method for the screening is so-called Pap smear test via examining morphology change in cervix cells. The aim of this research is to implement an image processing algorithm for classifying Pap smear cell images by calculating nucleus-to-cytoplasm area ratio. The algorithm used to classify the nucleus was mathematically calculated through k-mean clustering. The cytoplasm area was calculated from its edge profile relating to geometrical rotation method. Finally, the abnormal cells can be segmented using the area of nucleus-to-cytoplasm ratio with the accuracy of detection at 79%.
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    Low-cost instrumentation of Automated Whole - Slide Microscope for Biomedical Imaging
    (2019-01-10)
    Pechprasarn, Suejit
    ;
    Sukkasem, Chayanisa
    ;
    Suvarnaphaet, Phitsini
    ;
    Thongchoom, Rujirada
    ;
    Chuwittaya, Suteeboon
    Microscope is one of the essential devices in medical profession. It is usually used for finding out types of abnormal cells or tissues, detecting the diseases, and defining a range treatment option for patient. Recently, there is a new technology of whole-slide imaging (WSI) which has been developed in the microscope. The technology provides the scanning of conventional glass slides to produce digital images of cumulative data. Since the WSI is a complex system and very expensive price, hence we aim to implement the prototype of a cost- effective whole - slide imaging system based on the principle of microscopic design. The implemented prototype consisting of optical and mechanical parts was developed using 3D printing. The optical system employed a 40x objective lens aligning with the digital camera for imaging the specimen on the standard slide. The mechanical system was designed for the movement of the slide in 3 dimensions automatically and controlled by stepper motors and microcontrollers based on controlling computer program. To accumulate the whole- slide image, the slide was scanned and captured in the X-Y axes and the focal (Z) axis in a sequence. The cumulative data was then analyzed and rendered the whole- slide image. The implemented device would be advantageous to cytologists and doctors for the biomedical imaging and recording numerous medical data of the patients.