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

Tetik B., Ucuzal H., YAŞAR Ş., ÇOLAK C.

KONURALP TIP DERGISI, cilt.13, sa.2, ss.192-200, 2021 (ESCI) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 13 Sayı: 2
  • Basım Tarihi: 2021
  • Doi Numarası: 10.18521/ktd.889777
  • Dergi Adı: KONURALP TIP DERGISI
  • Derginin Tarandığı İndeksler: Emerging Sources Citation Index (ESCI), TR DİZİN (ULAKBİM)
  • Sayfa Sayıları: ss.192-200
  • Anahtar Kelimeler: Brain Tumors, Deep-Learning Strategy, Keras/Auto-Keras, T1-Weighted Magnetic Resonance Imaging, CENTRAL-NERVOUS-SYSTEM, EPIDEMIOLOGY
  • İnönü Üniversitesi Adresli: Evet

Özet

Objective: Primary central nervous system tumors (PCNSTs) compose nearly 3% of newly diagnosed cancers worldwide and are more common in men. The incidence of brain tumors and PCNSTs-related deaths are gradually increasing all over the world. Recently, many studies have focused on automated machine learning (AutoML) algorithms which are developed using deep learning algorithms on medical imaging applications. The main purposes of this study are -to demonstrate the use of artificial intelligence-based techniques to predict medical images of different brain tumors (glioma, meningioma, pituitary adenoma) to provide techicalsupport to radiologists and -to develop a user-friendly and free web-based software to classify brain tumors for making quick and accurate clinical decisions.