Classification of the brain metastases based on a new 3D deep learning architecture

Cuşkun Y., Kaplan K., Alparslan B., Ertunç H. M.

Soft Computing, cilt.27, sa.22, ss.17243-17256, 2023 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 27 Sayı: 22
  • Basım Tarihi: 2023
  • Doi Numarası: 10.1007/s00500-023-08051-w
  • Dergi Adı: Soft Computing
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Applied Science & Technology Source, Compendex, Computer & Applied Sciences, INSPEC, zbMATH
  • Sayfa Sayıları: ss.17243-17256
  • Anahtar Kelimeler: Brain metastases, Classification, CNN, Deep learning, Medical imaging
  • Kocaeli Üniversitesi Adresli: Evet

Özet

Malignant tumors can cause to brain tumors by the spreading to the brain. When the primary site of origin of brain metastases are differentiated by deep learning algorithms, doctors can begin treatment in time without performing biopsy. In this study, we aimed to classify the primary site of brain metastases from the MR images of patients with brain tumors. The MR images were read by radiologists from Kocaeli University Department of Radiology by using their Picture Archiving and Communication System (PACS). We used the three-dimensional convolutional neural networks technique, which is one of the artificial intelligence techniques that are frequently used to determine image classification and identify segmentation problems for the classification using a data set consisting of labeled MR images. Contrary to the models used in the studies performed on MR images in the literature, four different sequences were submitted to the classification model after four different convolutional processes were conducted, although not simultaneously. The classification scheme was designed as a binary classification and a three group classifications. The results showed that 81.48% accuracy rate was obtained in the binary classification and 74.07% accuracy was obtained in the three-group classification. To improve the accuracy rate of the three group classifications, data augmentation was applied and the accuracy rate improved to 75.92%.