2019 International Conference on Intelligent Systems and Advanced Computing Sciences (ISACS)最新文献

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Using Multi-Feature Selection with machine learning for De novo Acute Myeloid Leukemia in Egypt 多特征选择与机器学习在埃及新生急性髓性白血病中的应用

2019 International Conference on Intelligent Systems and Advanced Computing Sciences (ISACS) Pub Date : 2019-12-01 DOI: 10.1109/ISACS48493.2019.9068905

P. El-Kafrawy, Ibrahim I. M. Manhrawy, Hanaa Fathi, Mohammed Qaraad, A. Kelany

{"title":"Using Multi-Feature Selection with machine learning for De novo Acute Myeloid Leukemia in Egypt","authors":"P. El-Kafrawy, Ibrahim I. M. Manhrawy, Hanaa Fathi, Mohammed Qaraad, A. Kelany","doi":"10.1109/ISACS48493.2019.9068905","DOIUrl":"https://doi.org/10.1109/ISACS48493.2019.9068905","url":null,"abstract":"De novo Acute Myeloid Leukemia is one of the diseases from which many people die each year. It is the most common type of all types of cancer and causes death of people all over the world. Classification methods are an efficient means to separate data. Especially in the field of medicine, where these methods are widely used in diagnosis and analysis for decision-making. In this paper, we consider group feature selection in a multiclass classification of other ways. The performance will be compared between different machine learning algorithms: Random Forest classifier (RF), Logistic Regression (LR), Decision Tree (DT), Support Vector Machine (SVM) and Naive Bayes (NB) on AML dataset National Cancer Institute (NCI), Cairo University. The main objective is to evaluate the correction in the classification of the data concerning the efficiency and effectiveness of each algorithm in terms of accuracy, precision, sensitivity, and specificity. Experimental results determine that LR gives the enormous accuracy (92.30%) with the lowest error rate. All experiments are affected within a simulation environment and manipulated in Python 3.7 data mining tool.","PeriodicalId":312521,"journal":{"name":"2019 International Conference on Intelligent Systems and Advanced Computing Sciences (ISACS)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121179810","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

Brain Tumor Segmentation Based on Deep Learning 基于深度学习的脑肿瘤分割

2019 International Conference on Intelligent Systems and Advanced Computing Sciences (ISACS) Pub Date : 2019-12-01 DOI: 10.1109/ISACS48493.2019.9068878

Hajar Cherguif, J. Riffi, Mohamed Adnane Mahraz, Ali Yahyaouy, H. Tairi

{"title":"Brain Tumor Segmentation Based on Deep Learning","authors":"Hajar Cherguif, J. Riffi, Mohamed Adnane Mahraz, Ali Yahyaouy, H. Tairi","doi":"10.1109/ISACS48493.2019.9068878","DOIUrl":"https://doi.org/10.1109/ISACS48493.2019.9068878","url":null,"abstract":"Brain tumors develop rapidly and aggressively, causing brain damage and can be life threatening. Determining the extent of the tumor is a major challenge in brain tumor treatment planning and quantitative assessment to ameliorate the quality of life of patients. Magnetic resonance imaging (MRI) is an imaging technique widely used to evaluate these brain tumors, but manual segmentation prevented by the large amount of data generated by the MRI is a very long task and the performance is highly dependent on operator's experience. In this context, a reliable automatic segmentation method for segmenting the brain tumor is necessary for effective measurement of the extent of the tumor. There are several image segmentation algorithms, each with its own advantages and limitations. In this paper, we propose a method based on Deep Learning, using deep convolution networks based on the U-Net model. Our method was evaluated on real images provided by Medical Image Computing and Computer-Assisted Interventions BRATS 2017 datasets, which contain both HGG and LGG patients. Based on the experiments, our method can provide a segmentation that is both efficient and robust compared to the manually delineated ground truth. Our model showed a maximum Dice Similarity Coefficient metric of 0.81805 and 0.8103 for the dataset used.","PeriodicalId":312521,"journal":{"name":"2019 International Conference on Intelligent Systems and Advanced Computing Sciences (ISACS)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127939913","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 10

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