Computational Intelligence Method for Detection of White Blood Cells Using Hybrid of Convolutional Deep Learning and SIFT

Infection diseases are among the top global issues with negative impacts on health, economy, and society as a whole. One of the most effective ways to detect these diseases is done by analysing the microscopic images of blood cells. Artificial intelligence (AI) techniques are now widely used to detect these blood cells and explore their structures. In recent years, deep learning architectures have been utilized as they are powerful tools for big data analysis. In this work, we are presenting a deep neural network for processing of microscopic images of blood cells. Processing these images is particularly important as white blood cells and their structures are being used to diagnose different diseases. In this research, we design and implement a reliable processing system for blood samples and classify five different types of white blood cells in microscopic images. We use the Gram-Schmidt algorithm for segmentation purposes. For the classification of different types of white blood cells, we combine Scale-Invariant Feature Transform (SIFT) feature detection technique with a deep convolutional neural network. To evaluate our work, we tested our method on LISC and WBCis databases. We achieved 95.84% and 97.33% accuracy of segmentation for these data sets, respectively. Our work illustrates that deep learning models can be promising in designing and developing a reliable system for microscopic image processing.

Structural Heterogeneity of an Amorphous-Nanocrystalline Alloy Fe77Cu1Si16B6 in the Nanometer Range

In this article, an alloy of the Finemet type Fe77Cu1Si16B6 obtained by quenching from a liquid state (spinning method) in the initial state is investigated. The main research methods were scanning and transmission electron microscopy. Methods for describing multiscale structural heterogeneities in amorphous-nanocrystalline alloys have been developed, allowing the structural state to be described and its influence on the physicochemical and technical properties to be determined depending on the technological conditions for obtaining these alloys. Representation of electron microscopic images in the form of Fourier spectra made it possible to reveal the nature of the formation of short- and middle-order in amorphous-nanocrystalline alloys according to the principle of self-similar spatial structures. The analysis of electron microscopic images by integral Lebesgue measures revealed density fluctuations over the alloy volume, which corresponds to the hierarchical representation of structural inhomogeneities in amorphous metallic alloys.

Evaluation of acute and subchronic toxicity of “Tran Chau Nguu Hoang Hoan” in experimental animals

“Tran chau nguu hoang hoan” was prepared from 12 herbal ingredients. So far, the safety of this product, has not been reported yet. Thus, this study aimed to evaluate the acute and subchronic toxicity of “Tran chau nguu hoang hoan” through oral administration in experimental animals. The acute toxicity was determined by the method of Litchfield Wilcoxon in mice at the doses of 2.42 g/kg b.w/day to 6.04 g/kg b.w/day. The subchronic toxicity was evaluated followed the Guideline of WHO and OECD in rats with oral doses of 58.0 mg/kg b.w/day and 174.0 mg/kg b.w/day for 12 consecutive weeks. As a result, in the course of the acute toxicity test, the mice showed no abnormal sign or death. In terms of the subchonic toxicity test, hematological indexes, hepato-renal functions and microscopic images of liver and kidney were unchanged. In conclusion, “Tran chau nguu hoang hoan” does not appear to produce acute and subchronic toxicities in mice and rats.

Research on the Region-Growing and Segmentation Technology of Micro-Particle Microscopic Images Based on Color Features

Silkworm microparticle disease is a legal quarantine standard in the detection of silkworm disease all over the world. The current common detection method, the Pasteur manual microscopy method, has a low detection efficiency all over the world. The low efficiency of the current Pasteur manual microscopy detection method makes the application of machine vision technology to detect microparticle spores an important technology to advance silkworm disease research. For the problems of the low contrast, different illumination conditions and complex image background of microscopic images of the ellipsoidal symmetrical shape of silkworm microparticle spores collected in the detection solution, a region growth segmentation method based on microparticle color and grayscale information is proposed. In this method, the fuzzy contrast enhancement algorithm is used to enhance the color information of micro-particles and improve the discrimination between the micro-particles and background. In the HSV color space with stable color, the color information of micro-particles is extracted as seed points to eliminate the influence of light and reduce the interference of impurities to locate the distribution area of micro-particles accurately. Combined with the neighborhood gamma transformation, the highlight feature of the micro-particle target in the grayscale image is enhanced for region growing. Mea6nwhile, the accurate and complete micro-particle target is segmented from the complex background, which reduces the background impurity segmentation caused by a single feature in the complex background. In order to evaluate the segmentation performance, we calculate the IOU of the microparticle sample image segmented by this method with its corresponding true value image, and the experiments show that the combination of color and grayscale features using the region growth technique can accurately and completely segment the microparticle target in complex backgrounds with a segmentation accuracy IOU as high as 83.1%.

Development of segmentation algorithm for determining planktonic objects from microscopic images

Plankton are free-floating organisms that live, grow, and move along with the ocean currents. This free-floating organism plays important roles as primary producers, they serve as a link to energy transfer, and a factor that regulates the biogeochemical cycles. Indonesia, with almost 60% of its territory covered by the ocean, harbours a wide variety of planktonic species. However, one of the issues within usual planktonic studies is the lack of a fast and accurate method for identifying and classifying the plankton type. Thus, the computer vision methods on microscopic images were proposed to deal with the problem. The classification follows two main steps, detecting plankton location and followed by plankton differentiation. The segmentation algorithm is required to limit the determination area. The present study describes the segmentation methods on fifteen plankton types. The U-Net based architecture was implemented to segment the plankton texture from other objects. The segmentation result was also compared with the manual assessment to compute the performance parameters. The accuracy, 0.970±0.025, gives the highest value whereas the smallest value is found in the precision parameter, 0.761±0.156.