{"title":"Analysis of Medical Slide Images Processing using Depth Learning in Histopathological Studies of Cerebellar Cortex Tissue","authors":"Xiang Zhang, Xiaowei Shi, Xingyi Zhang","doi":"10.14569/ijacsa.2023.0140167","DOIUrl":null,"url":null,"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.","PeriodicalId":13824,"journal":{"name":"International Journal of Advanced Computer Science and Applications","volume":null,"pages":null},"PeriodicalIF":0.7000,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Advanced Computer Science and Applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.14569/ijacsa.2023.0140167","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, THEORY & METHODS","Score":null,"Total":0}
引用次数: 0
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.
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