디지털 홀로그래픽 현미경과 인공지능 기반의 혈구성 질환 진단

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딥러닝 적용을 통한 디지털 홀로그래픽 현미경 기법의 향상

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AI-based holography using an incoherent light source

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Intelligent Healthcare Platform for Diagnosis of Scalp and Hair Disorders

Various scalp and hair disorders distress numerous people. Severe scalp hair disorders have an adverse effect on appearance, self-confidence, and quality of life. Therefore, early and exact diagnosis of various scalp hair disorders is important for timely treatment. However, conventional manual examination method is time-consuming, objective, and labor-intensive. The presented study proposes an intelligent healthcare platform for identifying severity levels of six common scalp hair disorders such as dryness, oiliness, erythema, folliculitis, dandruff, and hair loss. To establish a suitable scalp image classification model, we tested three deep learning models (ResNet-152, EfficientNet-B6, and ViT-B/16). Among the three tested deep learning models, the ViT-B/16 model exhibited the best classification performance with an average accuracy of 78.31%. In addition, the attention rollout method was applied to explain the decision of the trained ViT-B/16 model and highlight approximate lesion areas with no additional annotation procedure. Finally, Scalp checker software was developed based on the trained ViT-B/16 model and the attention rollout method. Accordingly, this proposed platform facilitates objective monitoring states of the scalp and early diagnosis of hairy scalp problems

Volumetric monitoring of airborne particulate matter concentration using smartphone-based digital holographic microscopy and deep learning

Airborne particulate matter (PM) has become a global environmental issue. This PM has harmful effects on public health and precision industries. Conventional air-quality monitoring methods usually utilize expensive equipment, and they are cumbersome to handle for accurate and high throughput measurements. In addition, commercial particle counters have technical limitations in high-concentration measurement, and data fluctuations are induced during air sampling. In this study, a novel smartphone-based technique for monitoring airborne PM concentrations was developed using smartphone-based digital holographic microscopy (S-DHM) and deep learning network called Holo-SpeckleNet. Holographic speckle images of various PM concentrations were recorded by the S-DHM system. The recorded speckle images and the corresponding ground truth PM concentrations were used to train deep learning algorithms consisting of a deep autoencoder and regression layers. The performance of the proposed smartphone-based PM monitoring technique was validated through hyperparameter optimization. The developed S-DHM integrated with Holo-SpeckleNet can be smartly and effectively utilized for portable PM monitoring and safety alarm provision under perilous environmental conditions.11Nsciescopu

Three-dimensional volumetric monitoring of settling particulate matters on a leaf using digital in-line holographic microscopy

Plants are considered as a possible modality to reduce particulate matter (PM) particles from ambient air in an ecofriendly manner. A new precise monitoring technique that can explore interactions between individual PM particles and a leaf surface is necessary to understand the underlying mechanisms of PM removal of plant leaves. In this study, a digital in-line holographic microscopy (DIHM) was employed to experimentally investigate the settling motions of PM particles over the leaf surface. The in-plane positions and sizes of opaque PMs with irregular shapes were obtained from the projection images of numerically reconstructed holographic images. The depth positions of PMs were determined by using proper selection of an autofocusing criterion with automatic segmentation method. The edge of a hairy Perilla frutescens leaf was detected by adopting several digital imaging processing techniques. The DIHM technique was applied in this study to accurately detect 3D settling trajectories of PMs with velocity information of PMs in the midair and near leaf surface, simultaneously.11Nsciescopu

Digital stereo-holographic microscopy for studying three-dimensional particle dynamics

A digital stereo -holographic microscopy (DsHM) with two viewing angles is proposed to measure 3D information of microscale particles. This approach includes two volumetric recordings and numerical reconstruction, and it involves the combination of separately reconstructed holograms. The 3D positional information of a particle was determined by searching the center of the overlapped reconstructed volume. After confirming the proposed technique using static spherical particles, the 3D information of moving particles suspended in a Hagen-Poiseiulle flow was successfully obtained. Moreover, the 3D information of nonspherical particles, including ellipsoidal particles and red blood cells, were measured using the proposed technique. In addition to 3D positional information, the orientation and shape of the test samples were obtained from the plane images by slicing the overlapped volume perpendicular to the directions of the image recordings. This DsHM technique will be useful in analyzing the 3D dynamic behavior of various nonspherical particles, which cannot be measured by conventional digital holographic microscopy. (C) 2017 Elsevier Ltd. All rights reserved.11Nsciescopu

Label-free sensor for automatic identification of erythrocytes using digital in-line holographic microscopy and machine learning

Cell types of erythrocytes should be identified because they are closely related to their functionality and viability. Conventional methods for classifying erythrocytes are time consuming and labor intensive. Therefore, an automatic and accurate erythrocyte classification system is indispensable in healthcare and biomedical fields. In this study, we proposed a new label-free sensor for automatic identification of erythrocyte cell types using a digital in-line holographic microscopy (DIHM) combined with machine learning algorithms. A total of 12 features, including information on intensity distributions, morphological descriptors, and optical focusing characteristics, is quantitatively obtained from numerically reconstructed holographic images. All individual features for discocytes, echinocytes, and spherocytes are statistically different. To improve the performance of cell type identification, we adopted several machine learning algorithms, such as decision tree model, support vector machine, linear discriminant classification, and k-nearest neighbor classification. With the aid of these machine learning algorithms, the extracted features are effectively utilized to distinguish erythrocytes. Among the four tested algorithms, the decision tree model exhibits the best identification performance for the training sets (n = 440, 98.18%) and test sets (n = 190, 97.37%). This proposed methodology, which smartly combined DIHM and machine learning, would be helpful for sensing abnormal erythrocytes and computer-aided diagnosis of hematological diseases in clinic.115sciescopu

홀로그래픽 현미경과 기계학습형 인공 지능을 결합한 말라리아의 자동 진단 기술 개발

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Measurement of particular matter concentration based on holographic speckle pattern and deep learning

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