Canan Kocatürk, Cemre Candemir, İ̇lker Kocabaş
{"title":"Akut Lenfositik Löseminin Makine Öğrenimi Yöntemleriyle Otomatik Tespitine İlişkin Karşılaştırmalı Bir Çalışma","authors":"Canan Kocatürk, Cemre Candemir, İ̇lker Kocabaş","doi":"10.21205/deufmd.2022247229","DOIUrl":null,"url":null,"abstract":"Acute Lymphocytic Leukemia (ALL) is one of the most prevalent types of leukemia which has the risk of death of children is relatively higher than adults. The early diagnosis of this disease is crucial and it can be detected by examining the morphological changes of the blood cells. In this study, we exhibit a comparative study on the automatic classification and identification of the ALL with machine learning methodologies. Acute Lymphoblastic Challange Database (ALL-CDB) served by the Cancer Imaging Archive, which consists of 6500 digital microscopic pathology images from 118 subjects, is used. As the first step, the geometric features are extracted and after, the feature selection was performed with Principal Component Analysis (PCA). Finally, the classification process on the selected features was carried out by using Naive Bayes, k-Nearest Neighbor (k-NN), Linear Discriminant Analysis (LDA), Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), and Multilayer Perceptron (MLP) neural network methods. The results between the methodologies have been analyzed in terms of accuracy, precision, recall, and F1-score metrics. According to the results, MLP gives the both highest accuracy and F1-score with 97% to classify the ALL cells for leukemia.","PeriodicalId":23481,"journal":{"name":"Uluslararası Muhendislik Arastirma ve Gelistirme Dergisi","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2022-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Uluslararası Muhendislik Arastirma ve Gelistirme Dergisi","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.21205/deufmd.2022247229","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

急性淋巴细胞白血病(Acute Lymphocytic Leukemia, ALL)是最常见的白血病类型之一,儿童的死亡风险相对高于成人。这种疾病的早期诊断是至关重要的,它可以通过检查血细胞的形态变化来检测。在这项研究中,我们展示了机器学习方法对ALL的自动分类和识别的比较研究。使用由癌症影像档案馆提供的急性淋巴细胞病变数据库(ALL-CDB),该数据库由118名受试者的6500张数字显微病理图像组成。首先提取几何特征,然后利用主成分分析(PCA)进行特征选择。最后,利用朴素贝叶斯、k-最近邻(k-NN)、线性判别分析(LDA)、决策树(DT)、随机森林(RF)、支持向量机(SVM)和多层感知器(MLP)神经网络方法对所选特征进行分类处理。在准确度、精密度、召回率和f1评分指标方面分析了两种方法之间的结果。结果显示,MLP对ALL细胞的白血病分类准确率最高,f1评分为97%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Akut Lenfositik Löseminin Makine Öğrenimi Yöntemleriyle Otomatik Tespitine İlişkin Karşılaştırmalı Bir Çalışma
Acute Lymphocytic Leukemia (ALL) is one of the most prevalent types of leukemia which has the risk of death of children is relatively higher than adults. The early diagnosis of this disease is crucial and it can be detected by examining the morphological changes of the blood cells. In this study, we exhibit a comparative study on the automatic classification and identification of the ALL with machine learning methodologies. Acute Lymphoblastic Challange Database (ALL-CDB) served by the Cancer Imaging Archive, which consists of 6500 digital microscopic pathology images from 118 subjects, is used. As the first step, the geometric features are extracted and after, the feature selection was performed with Principal Component Analysis (PCA). Finally, the classification process on the selected features was carried out by using Naive Bayes, k-Nearest Neighbor (k-NN), Linear Discriminant Analysis (LDA), Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), and Multilayer Perceptron (MLP) neural network methods. The results between the methodologies have been analyzed in terms of accuracy, precision, recall, and F1-score metrics. According to the results, MLP gives the both highest accuracy and F1-score with 97% to classify the ALL cells for leukemia.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信