{"title":"车间机器学习克拉西卡肿瘤奥塔克帕特拉核磁共振蒙古纳坎卷积神经网络丹支持向量机","authors":"A. Minarno, Denar Regata Akbi, Yuda Munarko","doi":"10.37303/peduli.v6i1.421","DOIUrl":null,"url":null,"abstract":"The brain is one of the organs that very important role for humans. Brain tumors can be a threat for humans. So that The researchers developed a CNN method that was tested effective for detecting brain tumors. CNN is a method that is quite popular, in its application it is used for image classification and several other image processing cases. CNN can be used to detect and recognize objects in an image better on an Artificial Neural Network. In addition, many researchers also use the SVM method, SVM can be applied to perform pattern recognition in the case of image processing. Brain tumors can be caused by the spread of cancer in parts of the other body. According to a report by the World Health Organization (WHO) brain cancer accounts for less than 2% of other cancers, but the severe morbidity and resulting complications are enormous. Brain cancer requires multidisciplinary treatment, so a professional standard policy is needed for optimal treatment. This activity proposes a Machine Learning Workshop on Brain Tumor Classification in MRI Imagery using CNN and SVM, in CNN activities can be divided into several parts such as CNN modeling, data preprocessing, building, and implementing CNN models in The SVM teaches how to build a hyperplane. This activity was delivered by expert speakers in their fields from alumni of the University of Muhammadiyah Malang.","PeriodicalId":260058,"journal":{"name":"PEDULI: Jurnal Ilmiah Pengabdian Pada Masyarakat","volume":"3 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"WORKSHOP MACHINE LEARNING KLASIFIKASI TUMOR OTAK PADA CITRA MRI MENGGUNAKAN CONVOLUTIONAL NEURAL NETWORK DAN SUPPORT VECTOR MACHINE\",\"authors\":\"A. Minarno, Denar Regata Akbi, Yuda Munarko\",\"doi\":\"10.37303/peduli.v6i1.421\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The brain is one of the organs that very important role for humans. Brain tumors can be a threat for humans. So that The researchers developed a CNN method that was tested effective for detecting brain tumors. CNN is a method that is quite popular, in its application it is used for image classification and several other image processing cases. CNN can be used to detect and recognize objects in an image better on an Artificial Neural Network. In addition, many researchers also use the SVM method, SVM can be applied to perform pattern recognition in the case of image processing. Brain tumors can be caused by the spread of cancer in parts of the other body. According to a report by the World Health Organization (WHO) brain cancer accounts for less than 2% of other cancers, but the severe morbidity and resulting complications are enormous. Brain cancer requires multidisciplinary treatment, so a professional standard policy is needed for optimal treatment. This activity proposes a Machine Learning Workshop on Brain Tumor Classification in MRI Imagery using CNN and SVM, in CNN activities can be divided into several parts such as CNN modeling, data preprocessing, building, and implementing CNN models in The SVM teaches how to build a hyperplane. This activity was delivered by expert speakers in their fields from alumni of the University of Muhammadiyah Malang.\",\"PeriodicalId\":260058,\"journal\":{\"name\":\"PEDULI: Jurnal Ilmiah Pengabdian Pada Masyarakat\",\"volume\":\"3 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-09-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"PEDULI: Jurnal Ilmiah Pengabdian Pada Masyarakat\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.37303/peduli.v6i1.421\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"PEDULI: Jurnal Ilmiah Pengabdian Pada Masyarakat","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.37303/peduli.v6i1.421","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
WORKSHOP MACHINE LEARNING KLASIFIKASI TUMOR OTAK PADA CITRA MRI MENGGUNAKAN CONVOLUTIONAL NEURAL NETWORK DAN SUPPORT VECTOR MACHINE
The brain is one of the organs that very important role for humans. Brain tumors can be a threat for humans. So that The researchers developed a CNN method that was tested effective for detecting brain tumors. CNN is a method that is quite popular, in its application it is used for image classification and several other image processing cases. CNN can be used to detect and recognize objects in an image better on an Artificial Neural Network. In addition, many researchers also use the SVM method, SVM can be applied to perform pattern recognition in the case of image processing. Brain tumors can be caused by the spread of cancer in parts of the other body. According to a report by the World Health Organization (WHO) brain cancer accounts for less than 2% of other cancers, but the severe morbidity and resulting complications are enormous. Brain cancer requires multidisciplinary treatment, so a professional standard policy is needed for optimal treatment. This activity proposes a Machine Learning Workshop on Brain Tumor Classification in MRI Imagery using CNN and SVM, in CNN activities can be divided into several parts such as CNN modeling, data preprocessing, building, and implementing CNN models in The SVM teaches how to build a hyperplane. This activity was delivered by expert speakers in their fields from alumni of the University of Muhammadiyah Malang.