Analysis of Medical Slide Images Processing using Depth Learning in Histopathological Studies of Cerebellar Cortex Tissue

IF 0.7 Q3 COMPUTER SCIENCE, THEORY & METHODS International Journal of Advanced Computer Science and Applications Pub Date : 2023-01-01 DOI:10.14569/ijacsa.2023.0140167
Xiang Zhang, Xiaowei Shi, Xingyi Zhang
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Abstract

—Today, with the advancement of science and technology, artificial intelligence evolves and grows along with human beings. Clinical specialists rely only on their knowledge and experience, as well as the results of complex and time-consuming clinical trials, despite the inevitable human errors of diagnosis work. Performing malignant and dangerous diseases, the use of machine learning makes it clear that the ability and capacity of these techniques are beneficial to help correctly diagnose diseases, reduce human error, improve diagnosis, and start treatment as soon as possible. In diseases, image processing and artificial intelligence is widely used in medicine and applied in stereological, histopathology. One of the essential activities for diagnosing the disease using artificial intelligence and machine learning is the fragmentation of images and classification of medical images, which is used to diagnose the disease with the help of images of the patient obtained from medical devices. In this article, we have worked on classifying medical histopathological images of brain tissue. The images are not of good quality due to sampling with standard equipment, and an attempt is made to improve the quality of the images by operating. Also, all images are segmented using the U-NET algorithm. In order to improve performance in classification, segmented images are used to classify images into two classes, normal and abnormal, instead of the images themselves. The images in the data set used in this study have a small number of images. Due to the use of a convolutional neural network algorithm to extract the feature and classify the images, more images are needed. Therefore, the data amplification technique to overcome this problem is used. Finally, the convolutional neural network has been used to extract features from images and classify fragmented images. Experimental results shown that the proposed method presented better performance compared to other existing methods.
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在小脑皮质组织病理学研究中应用深度学习的医学切片图像处理分析
——在科技进步的今天,人工智能与人类共同进化、共同成长。临床专家只依赖他们的知识和经验,以及复杂和耗时的临床试验的结果,尽管诊断工作不可避免地存在人为错误。在处理恶性和危险的疾病时,机器学习的使用清楚地表明,这些技术的能力和能力有利于帮助正确诊断疾病,减少人为错误,提高诊断水平,并尽快开始治疗。在疾病方面,图像处理和人工智能在医学上应用广泛,在立体学、组织病理学上也有应用。利用人工智能和机器学习进行疾病诊断的重要活动之一是医学图像的碎片化和分类,利用从医疗设备中获得的患者图像进行疾病诊断。在本文中,我们对脑组织的医学组织病理图像进行了分类。由于使用标准设备采样,图像质量不佳,尝试通过操作来提高图像质量。此外,所有图像都使用U-NET算法进行分割。为了提高分类性能,使用分割图像将图像分为正常和异常两类,而不是图像本身。本研究使用的数据集中的图像数量较少。由于使用卷积神经网络算法提取特征并对图像进行分类,需要更多的图像。因此,采用数据放大技术来克服这一问题。最后,利用卷积神经网络从图像中提取特征并对碎片图像进行分类。实验结果表明,与现有方法相比,该方法具有更好的性能。
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来源期刊
CiteScore
2.30
自引率
22.20%
发文量
519
期刊介绍: IJACSA is a scholarly computer science journal representing the best in research. Its mission is to provide an outlet for quality research to be publicised and published to a global audience. The journal aims to publish papers selected through rigorous double-blind peer review to ensure originality, timeliness, relevance, and readability. In sync with the Journal''s vision "to be a respected publication that publishes peer reviewed research articles, as well as review and survey papers contributed by International community of Authors", we have drawn reviewers and editors from Institutions and Universities across the globe. A double blind peer review process is conducted to ensure that we retain high standards. At IJACSA, we stand strong because we know that global challenges make way for new innovations, new ways and new talent. International Journal of Advanced Computer Science and Applications publishes carefully refereed research, review and survey papers which offer a significant contribution to the computer science literature, and which are of interest to a wide audience. Coverage extends to all main-stream branches of computer science and related applications
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