Analysis of images obtained by the transmission electronic microscopy

Abdikerimova, Gulzira; Бычков, А. Л.; Khayrulin, Sergey; Murzin, F.A.; Russkikh, Nikolay; Ryabchikova, E. I.; Wang, Xinyu

doi:10.1016/j.matpr.2019.03.071

Cited by 4 publications

(3 citation statements)

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“…The article [1] provides an analysis of the nonlinear segmentation of textured regions on microphotography in the processing of systems. Here are some questions about the use of experimental data from the experimental data collection on microphones, which can be supposed to be related to porosity, chemical reaction, and so on.…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

Segmentation of aerospace images by a non-standard approach using informative textural features

Yerzhanova

Abdikerimova

Alimova

et al. 2022

EEJET

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The article presents an analysis of a non-standard approach to the segmentation of textural areas in aerospace images. The question of the applicability of sets of textural features for the analysis of experimental data is being investigated to identify characteristic areas on aerospace images that in the future it will be possible to identify types of crops, weeds, diseases, and pests. The selection of suitable algorithms was carried out and appropriate software tools were created on Matlab 2021a and in the software package for statistical analysis Statistica 12. The main way to extract information is to decrypt images, which are the main carrier of information about the underlying surface. The main tasks of texture area analysis include selection and formation of features describing textural differences; selection and segmentation of textural areas; classification of textural areas; identification of an object by texture. To solve the tasks, spectral brightness coefficient (SBC), Normalized Difference Vegetation Index (NDVI), textural features of various crops and weeds. Much attention will be paid to the development of software tools that allow the selection of features describing textural differences for the segmentation of textural areas into subdomains. That is the question of the applicability of sets of textural features and other parameters for the analysis of experimental data to identify types of soils and soils, vegetation types, humidity, crop damage in aerospace images will be resolved. This approach is universal and has great potential for identifying objects using image clustering. To identify the boundaries of areas with different properties of the image under study, images of the same surface area taken at different times are considered.

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Section: Literature Review and Problem Statementmentioning

confidence: 99%

Segmentation of aerospace images by a non-standard approach using informative textural features

Yerzhanova

Abdikerimova

Alimova

et al. 2022

EEJET

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“…Assessing the condition of the retina and identifying pathologies requires accuracy and high sensitivity. In this context, deep learning methods [9], [10] provide a unique opportunity for automated analysis of retinal structures [11] with a high degree of accuracy. EfficientNet, originally designed to achieve an optimal balance between accuracy and computational efficiency, provides efficient neural network architectures [12]- [14].…”

Section: Introductionmentioning

confidence: 99%

Application of deep learning methods for automated analysis of retinal structures in ophthalmology

Kassymova,

Konyrkhanova,

Issembayeva

et al. 2024

IJECE

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This article examines a current area of research in the field of ophthalmology the use of deep learning methods for automated analysis of retinal structures. This work explores the use of deep learning methods such as EfficientNet and DenseNet for the automated analysis of retinal structures in ophthalmology. EfficientNet, originally proposed to balance between accuracy and computational efficiency, and DenseNet, based on dense connections between layers, are considered as tools for identifying and classifying retina features. Automated analysis includes identifying pathologies, assessing the degree of their development and, possibly, diagnosing various eye diseases. Experiments are performed on a dataset containing a variety of images of retinal structures. Results are evaluated using metrics of accuracy, sensitivity, and specificity. It is expected that the proposed deep learning methods can significantly improve the automated analysis of retinal images, which is important for the diagnosis and monitoring of eye diseases. As a result, the article highlights the significance and promise of using deep learning methods in ophthalmology for automated analysis of retinal structures. These methods help improve the early diagnosis, treatment and monitoring of eye diseases, which can ultimately lead to improved healthcare quality and improved patient lives.

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“…For the diagnosis of breast cancer, DSS is very important as this diagnosis is the most common cause of death for women worldwide. Analyzing the capabilities of these systems, we can say that fuzzy logic is a high-quality computational approach 10,11,12 .…”

Section: Introductionmentioning

confidence: 99%

Imaging fuzzy expert system for assessing dynamic changes in biomedical tumor images in breast cancer

Orazayeva

Wójcik

Pavlov

et al. 2022

Photonics Applications in Astronomy, Communications, Industry, and High Energy Physics Experiments 2022

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The purpose of this study is to develop a fuzzy expert system based on the analysis of biomedical images for the diagnosis of oncological diseases using breast cancer as an example. The main directions of application of mathematical methods in medical diagnosis are analyzed, their drawbacks are evaluated, and principles of diagnosis, based on fuzzy logic, are formulated. Mathematical models and algorithms are developed, formalizing the process of diagnostic decision-making based on fuzzy logic with quantitative and qualitative parameters of the patient's condition; mathematical models of membership functions are developed, formalizing the representation of quantitative and qualitative parameters of the patient's condition in the form of fuzzy sets, used in models and algorithms for diagnosing and determining the diagnosis in the case of breast cancer.

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Analysis of images obtained by the transmission electronic microscopy

Cited by 4 publications

References 1 publication

Segmentation of aerospace images by a non-standard approach using informative textural features

Segmentation of aerospace images by a non-standard approach using informative textural features

Application of deep learning methods for automated analysis of retinal structures in ophthalmology

Imaging fuzzy expert system for assessing dynamic changes in biomedical tumor images in breast cancer

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