Automated Classification of Brain Tumors by Deep Learning- Based Models on Magnetic Resonance Images Using a DevelopedWeb-Based Interface

YAŞAR, Şeyma; ÇOLAK, Cemil; TETİK, Bora; Ucuzal, Hasan

doi:10.18521/ktd.889777

Automated Classification of Brain Tumors by Deep Learning- Based Models on Magnetic Resonance Images Using a DevelopedWeb-Based Interface

Bora TETİK, (İnönü Üniversitesi, Tıp Fakültesi, Nöroşirürji Bölümü, Malatya, Türkiye)

Hasan UCUZAL, (İnönü Üniversite, Tıp Bilişimi Tıp Fakültesi, Biyoistatistik Anabilim Dalı, Malatya, Türkiye)

Şeyma YAŞAR, (İnönü Üniversite, Tıp Bilişimi Tıp Fakültesi, Biyoistatistik Anabilim Dalı, Malatya, Türkiye)

Cemil ÇOLAK (İnönü Üniversite, Tıp Bilişimi Tıp Fakültesi, Biyoistatistik Anabilim Dalı, Malatya, Türkiye)

KONURALP TIP DERGİSİ

20 5

Yıl: 2021 Cilt: 13 Sayı: 2 Sayfa Aralığı: 192 - 200 Metin Dili: İngilizce DOI: 10.18521/ktd.889777 İndeks Tarihi: 06-12-2021

Automated Classification of Brain Tumors by Deep Learning- Based Models on Magnetic Resonance Images Using a DevelopedWeb-Based Interface

Öz:

Objective: Primary central nervous system tumors (PCNSTs)compose nearly 3% of newlydiagnosed cancers worldwide and are more common in men. The incidence of brain tumors andPCNSTs-related deaths are gradually increasing all over the world. Recently, many studies havefocused on automated machine learning (AutoML) algorithms which are developed using deeplearning algorithms on medical imaging applications. The main purposes of this study are -todemonstrate the use of artificial intelligence-based techniques to predict medical images ofdifferent brain tumors (glioma, meningioma, pituitary adenoma) to provide techicalsupport toradiologists and -to develop a user-friendly and free web-based software to classify brain tumorsfor making quick and accurate clinical decisions.Methods: Open-sourced T1-weighted magnetic resonance brain tumor images were achieved fromNanfang Hospital, Guangzhou, China, and General Hospital, Tianjin Medical University,Toconstruct the proposed system which web-based interface and the deep learning-based models, theKeras/Auto-Keras library, which is employed in Python's programming language, is used.Accuracy, sensitivity, specificity, G-mean, F-score, and Matthews correlation coefficient metricswere used for performance evaluations.Results: While 80% (2599 instances) of the dataset was used in the training phase, 20% (465instances) was employed in the testing phase. All the performance metrics were higher than 98%for the classification of brain tumors on the training data set. Similarly, all the evaluation metricswere higher than 91% except for sensitivity and MCC for meningioma on the testing dataset.Conclusions: The results from the experiment reveal that the proposed software can be used todetect and diagnose three types of brain tumors. This developed web-based software can beaccessed freely in both English and Turkish at http://biostatapps.inonu.edu.tr/BTSY/.

Anahtar Kelime:

Geliştirilmiş Bir Web Tabanlı Arayüz Kullanarak Beyin Tümörlerinin Manyetik Rezonans Görüntülerinde Derin Öğrenme Tabanlı Modellerle Otomatik Sınıflandırılması

Öz:

Amaç: Primer santral sinir sistemi tümörleri (PSSST), dünyada yeni teşhis edilen kanserlerinyaklaşık %3'ünü oluşturmaktadır ve erkeklerde sıklığı daha yüksektir. Beyin tümörlerinin vePSSST'lere bağlı ölümlerin görülme sıklığı tüm dünyada giderek artmaktadır. Son zamanlardabirçok çalışma, tıbbi görüntüleme uygulamalarında derin öğrenme algoritmaları kullanılarakgeliştirilen otomatik makine öğrenimi (AutoML) algoritmalarına odaklanmıştır. Bu çalışmanıntemel amacı, radyologlara destek sağlamak için beyin tümörlerinin (glioma, menenjiom hipofizadenomları) tıbbi görüntülerinin analizinde yapay zeka tabanlı tekniklerin kullanımını göstermek,hızlı ve doğru tanı konulması için beyin tümörlerini sınıflandıran kullanıcı dostu ve ‘ücretsiz webtabanli bir yazılım geliştirmektir.Gereç ve Yöntem: Açık kaynaklı T1 ağırlıklı manyetik rezonans beyin tümörü görüntüleriNanfang Hastanesi, Guangzhou, Çin ve Genel Hastane, Tianjin Tıp Üniversitesinden elde edildi.Önerilen web tabanlı arayüzün ve derin öğrenme tabanlı modellerin oluşturulması için Python'unprogramlama dilinde kullanılan Keras / Auto-Keras kütüphanesi kullanıldı. Performansdeğerlendirmelerinde doğruluk, duyarlılık, özgüllük, G-ortalama, F-skor ve Matthews korelasyonkatsayısı ölçümleri kullanıldı. Bulgular: Eğitim aşamasında veri kümesinin %80'i (2599 örnek) kullanılırken,%20'si (465örnek) test aşamasında kullanıldı. Eğitim veri setinde beyin tümörlerinin sınıflandırılmasında tümperformans ölçütleri %98'in üzerinde sonuçlanmıştır. Benzer şekilde, test veri setinde menenjiomiçin duyarlılık ve MCC dışındaki tüm değerlendirme ölçütleri % 91'den yüksektir.Sonuç: Deneysel sonuçlar, önerilen yazılımın üç tip beyin tümörünü tespit etmek ve tanı koymakiçin kullanılabileceğini ortaya koymaktadır. Geliştirilen bu web tabanlı yazılıma hem İngilizcehem de Türkçe olarak http://biostatapps.inonu.edu.tr/BTSY/ adresinden ücretsiz olarak erişilebilir.

Anahtar Kelime:

Belge Türü: Makale Makale Türü: Araştırma Makalesi Erişim Türü: Erişime Açık

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APA	TETİK B, Ucuzal H, YAŞAR Ş, ÇOLAK C (2021). Automated Classification of Brain Tumors by Deep Learning- Based Models on Magnetic Resonance Images Using a DevelopedWeb-Based Interface. , 192 - 200. 10.18521/ktd.889777
Chicago	TETİK Bora,Ucuzal Hasan,YAŞAR Şeyma,ÇOLAK Cemil Automated Classification of Brain Tumors by Deep Learning- Based Models on Magnetic Resonance Images Using a DevelopedWeb-Based Interface. (2021): 192 - 200. 10.18521/ktd.889777
MLA	TETİK Bora,Ucuzal Hasan,YAŞAR Şeyma,ÇOLAK Cemil Automated Classification of Brain Tumors by Deep Learning- Based Models on Magnetic Resonance Images Using a DevelopedWeb-Based Interface. , 2021, ss.192 - 200. 10.18521/ktd.889777
AMA	TETİK B,Ucuzal H,YAŞAR Ş,ÇOLAK C Automated Classification of Brain Tumors by Deep Learning- Based Models on Magnetic Resonance Images Using a DevelopedWeb-Based Interface. . 2021; 192 - 200. 10.18521/ktd.889777
Vancouver	TETİK B,Ucuzal H,YAŞAR Ş,ÇOLAK C Automated Classification of Brain Tumors by Deep Learning- Based Models on Magnetic Resonance Images Using a DevelopedWeb-Based Interface. . 2021; 192 - 200. 10.18521/ktd.889777
IEEE	TETİK B,Ucuzal H,YAŞAR Ş,ÇOLAK C "Automated Classification of Brain Tumors by Deep Learning- Based Models on Magnetic Resonance Images Using a DevelopedWeb-Based Interface." , ss.192 - 200, 2021. 10.18521/ktd.889777
ISNAD	TETİK, Bora vd. "Automated Classification of Brain Tumors by Deep Learning- Based Models on Magnetic Resonance Images Using a DevelopedWeb-Based Interface". (2021), 192-200. https://doi.org/10.18521/ktd.889777

APA	TETİK B, Ucuzal H, YAŞAR Ş, ÇOLAK C (2021). Automated Classification of Brain Tumors by Deep Learning- Based Models on Magnetic Resonance Images Using a DevelopedWeb-Based Interface. KONURALP TIP DERGİSİ, 13(2), 192 - 200. 10.18521/ktd.889777
Chicago	TETİK Bora,Ucuzal Hasan,YAŞAR Şeyma,ÇOLAK Cemil Automated Classification of Brain Tumors by Deep Learning- Based Models on Magnetic Resonance Images Using a DevelopedWeb-Based Interface. KONURALP TIP DERGİSİ 13, no.2 (2021): 192 - 200. 10.18521/ktd.889777
MLA	TETİK Bora,Ucuzal Hasan,YAŞAR Şeyma,ÇOLAK Cemil Automated Classification of Brain Tumors by Deep Learning- Based Models on Magnetic Resonance Images Using a DevelopedWeb-Based Interface. KONURALP TIP DERGİSİ, vol.13, no.2, 2021, ss.192 - 200. 10.18521/ktd.889777
AMA	TETİK B,Ucuzal H,YAŞAR Ş,ÇOLAK C Automated Classification of Brain Tumors by Deep Learning- Based Models on Magnetic Resonance Images Using a DevelopedWeb-Based Interface. KONURALP TIP DERGİSİ. 2021; 13(2): 192 - 200. 10.18521/ktd.889777
Vancouver	TETİK B,Ucuzal H,YAŞAR Ş,ÇOLAK C Automated Classification of Brain Tumors by Deep Learning- Based Models on Magnetic Resonance Images Using a DevelopedWeb-Based Interface. KONURALP TIP DERGİSİ. 2021; 13(2): 192 - 200. 10.18521/ktd.889777
IEEE	TETİK B,Ucuzal H,YAŞAR Ş,ÇOLAK C "Automated Classification of Brain Tumors by Deep Learning- Based Models on Magnetic Resonance Images Using a DevelopedWeb-Based Interface." KONURALP TIP DERGİSİ, 13, ss.192 - 200, 2021. 10.18521/ktd.889777
ISNAD	TETİK, Bora vd. "Automated Classification of Brain Tumors by Deep Learning- Based Models on Magnetic Resonance Images Using a DevelopedWeb-Based Interface". KONURALP TIP DERGİSİ 13/2 (2021), 192-200. https://doi.org/10.18521/ktd.889777