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The main advantage of MIL-based approach over other classification methods is that MIL considers an image as a group of instances rather than a single instance, thus facilitating an effective learning process. The mi-Graph-based MIL approach is proposed for this classification. Two different implementations of MIL-based classification, viz. Patch-based MIL (PBMIL) and Superpixel-based MIL (SPBMIL), are made in this study. The combined feature set of LBP, SIFT and FD is used for the classification. The accuracies of low-grade\u2013high-grade tumor image classification algorithm using SPBMIL method performed on [Formula: see text], [Formula: see text] and FLAIR images read 99.2765%, 99.4195% and 99.2265%, respectively. The error rate of the proposed classification system was noted to be insignificant and hence this automated classification system could be used for the classification of images with different pathological conditions, types and disease statuses.<\/jats:p>","DOI":"10.1142\/s2196888822500117","type":"journal-article","created":{"date-parts":[[2021,10,24]],"date-time":"2021-10-24T13:14:42Z","timestamp":1635081282000},"page":"163-186","source":"Crossref","is-referenced-by-count":2,"title":["Classification of Low-Grade and High-Grade Glioma from MR Brain Images Using Multiple-Instance Learning with Combined Feature Set"],"prefix":"10.1142","volume":"09","author":[{"given":"C. C.","family":"Benson","sequence":"first","affiliation":[{"name":"Department of Computer Science, University of Calicut, Tenjipalam PO, Malappuram 673635, Kerala, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"V. 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