{
    "article_id": 1277,
    "public_id": "0e6cf98f-8ed5-464d-802d-0d38bd79a4ed",
    "internal_identifier": null,
    "source_identifier": "84:139",
    "status": "published",
    "type": "research_article",
    "title": "Akut Lenfoblastik Löseminin Makine Öğrenimi ile Sınıflandırılması",
    "title_secondary": null,
    "abstract": "Akut Lenfoblastik Lösemi (ALL) genellikle 2-5 yaş arasındaki çocuklarda görülen, kemik iliğindeki beyaz kan hücrelerinin hızlı artışından ve şekilsel bozukluklarından kaynaklanan bir kanser türüdür. Tespit edilip tedavi sürecinin başlatılmadığı takdirde ölümlere yol açabilmekte olan bu hücrelerin normal hücrelere morfolojik açıdan benzemesi, hastalığın teşhisini zorlaştırdığı bilinmektedir. Bu çalışmada Navie Bayes (NB), Random Forest (RF) ve Destek Vektör Makinesi (Support Vector Machine – SVM) algoritmalarından yararlanılarak hücrelerin (kanserli-kansersiz) sınıflandırması yapılmıştır. Sınıflandırma için açık kaynaklı Cancer Imaging Archive tarafından sunulan ALL veri seti kullanılarak RGB ve Gray Scale renk uzaylarında sınıflandırma performansları test edilmiştir. Test sırasında her iki renk formatı içinde orijinal ve arttırılmış veri kullanılarak 2000, 6500, 10320, 12130 büyüklüğünde hastalıklı & sağlıklı hücrelerin oluşturduğu 4 farklı veri kümesi kullanılarak, her bir görüntünün 32x32 ve 128x128 ölçülerine yeniden boyutlandırılıp sınıflandırma işlemi gerçekleştirilmiştir. Yapılan deneysel çalışmalar sonucunda RGB renk uzayında 2000 boyutlu veri seti ve 128x128 boyutlarına ölçeklendirilmiş görüntüler üzerinde çalıştırılan SVM algoritması %89‘luk Accuracy oranı ile en iyi sonucu verdiği gözlenmiştir. ",
    "language": "tr",
    "english_title": "Classification of Acute Lymphoblastic Leukemia Using Machine Learning",
    "english_abstract": "Acute Lymphoblastic Leukemia (ALL) is a type of cancer that usually occurs in children aged 2-5 years, resulting from the rapid increase of lymphoblasts in the bone marrow and the deformities. Morphological resemblance of these cells to normal cells is known to complicate the diagnosis of the disease, if not detected and not the treatment process started, this can lead to death. In this study, Naive Bayes (NB), Random Forest (RF) and Support Vector Machine (SVM) algorithms are used to classify the cells (malign-benign). For classification, 12528 pathology images from 101 patients with ALL, presented by the open source Cancer Imaging Archive, were used. 8491 of these images were labeled as malign and 4037 of them were labeled as benign. Using ALL data set, classification performances in RGB (Red Green Blue) and Gray Scale color spaces were tested. During the test, the original and increased data in both color formats using 2000, 6500, 10320, 12130 size of malign & benign cells using 4 different data sets, each image was resized to 32x32 and 128x128 size classification was performed. As a result of the experimental studies, it was observed that the SVM algorithm, which was run on the 2000 dimensional dataset and the images scaled to 128x128 dimensions, gave the best result with 89% accuracy ratio.",
    "doi": "10.36287/setsci.4.6.139",
    "doi_url": "https://doi.org/10.36287/setsci.4.6.139",
    "resource_url": "https://www.set-science.com/a/0e6cf98f-8ed5-464d-802d-0d38bd79a4ed",
    "pdf_url": "https://www.set-science.com/a/0e6cf98f-8ed5-464d-802d-0d38bd79a4ed/pdf",
    "og_image": "https://www.set-science.com/assets/img/og-card-v2.png",
    "authors": [
        {
            "public_id": "085dc28f-c840-4626-bd27-086804aad5e8",
            "given": "Ayşe Berika",
            "family": "Varol Malkoçoğlu ",
            "full": "Ayşe Berika Varol Malkoçoğlu ",
            "literal": null,
            "orcid": null,
            "orcid_url": null,
            "email": "ayseberikavarol@maltepe.edu.tr",
            "corresponding": false,
            "sequence": "first",
            "affiliations": [
                {
                    "name": "Maltepe University",
                    "department": null,
                    "country": null,
                    "country_code": null,
                    "ror": null,
                    "ror_url": null
                }
            ]
        },
        {
            "public_id": "cebe1b98-0018-4b6c-8957-22464c2614db",
            "given": "İsmail",
            "family": "İşeri",
            "full": "İsmail İşeri",
            "literal": null,
            "orcid": null,
            "orcid_url": null,
            "email": "ismail.iseri@bil.omu.edu.tr",
            "corresponding": true,
            "sequence": "additional",
            "affiliations": [
                {
                    "name": "Ondokuz Mayıs University",
                    "department": null,
                    "country": null,
                    "country_code": null,
                    "ror": null,
                    "ror_url": null
                }
            ]
        }
    ],
    "keywords": [],
    "translations": [
        {
            "language": "en",
            "title": "Classification of Acute Lymphoblastic Leukemia Using Machine Learning",
            "abstract": "Acute Lymphoblastic Leukemia (ALL) is a type of cancer that usually occurs in children aged 2-5 years, resulting from the rapid increase of lymphoblasts in the bone marrow and the deformities. Morphological resemblance of these cells to normal cells is known to complicate the diagnosis of the disease, if not detected and not the treatment process started, this can lead to death. In this study, Naive Bayes (NB), Random Forest (RF) and Support Vector Machine (SVM) algorithms are used to classify the cells (malign-benign). For classification, 12528 pathology images from 101 patients with ALL, presented by the open source Cancer Imaging Archive, were used. 8491 of these images were labeled as malign and 4037 of them were labeled as benign. Using ALL data set, classification performances in RGB (Red Green Blue) and Gray Scale color spaces were tested. During the test, the original and increased data in both color formats using 2000, 6500, 10320, 12130 size of malign & benign cells using 4 different data sets, each image was resized to 32x32 and 128x128 size classification was performed. As a result of the experimental studies, it was observed that the SVM algorithm, which was run on the 2000 dimensional dataset and the images scaled to 128x128 dimensions, gave the best result with 89% accuracy ratio.",
            "keywords": [
                "Navie Bayes, Random Forest, Destek Vektör Makinesi, Sınıflandırma, Makine Öğrenimi"
            ]
        }
    ],
    "subjects": [],
    "subject_names": [],
    "published_date": "2019-12-22",
    "published_year": "2019",
    "received_date": null,
    "accepted_date": null,
    "volume": "9",
    "issue": null,
    "page_first": "497",
    "page_last": "501",
    "article_number": null,
    "conference": {
        "short_name": "ISAS WINTER-2019 (ENS)",
        "full_title": "4th International Symposium on Innovative Approaches in Engineering and Natural Sciences",
        "name": "4th International Symposium on Innovative Approaches in Engineering and Natural Sciences",
        "acronym": "ISAS WINTER-2019 (ENS)",
        "location": "Samsun, Türkiye",
        "start_date": "2019-11-22",
        "end_date": null,
        "public_id": "ede2435c-3820-4bef-84a9-59e7d8de9d60"
    },
    "proceedings": {
        "title": "4th International Symposium on Innovative Approaches in Engineering and Natural Sciences",
        "series_title": "SETSCI Conference Proceedings",
        "publisher": "SET Technology",
        "isbn": "978-605-81988-8-3",
        "eisbn": null,
        "issn": null,
        "eissn": "2687-5527",
        "doi": null,
        "doi_prefix": "10.36287",
        "slug": "setsci-volume-9-2019-legacy-84",
        "public_id": "5d3c128d-5090-429f-8398-4e32c57e5584",
        "url": "https://www.set-science.com/p/setsci-volume-9-2019-legacy-84"
    },
    "license": {
        "name": "CC BY 4.0",
        "url": "https://creativecommons.org/licenses/by/4.0/",
        "code": "CC-BY-4.0",
        "open_access": true
    },
    "copyright": {
        "holder": "SET Technology",
        "year": "2019"
    },
    "funding_statement": null,
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    "statements": {
        "acknowledgements": null,
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    "credit": [],
    "history": {
        "published": "2019-12-22"
    }
}