A mask R-CNN approach for detection and classification of brain tumours from MR images

Merve Kordemir, Kerim Kursat Cevik*, Ahmet Bozkurt

*Bu çalışma için yazışmadan sorumlu yazar

Araştırma sonucu: Dergiye katkıMakalebilirkişi

3 Atıf (Scopus)

Özet

This study aims to tackle the implementation of a single deep learning (DL) technique to the brain tumour recognition, segmentation and classification problem based on MR images. We propose utilising the Mask R-CNN approach along with transfer learning models on images. This was done by assigning bounding boxes and successfully constructing a border on each image for the tumour volume in order to distinguish it from neighbouring tissues and structures. By processing the images in this way, a single DL model could accurately identify, classify and segment three different categories of brain tumours (meningioma, glioma or pituitary). ResNet-50 and ResNet-101 network architectures were trained for 1000 epochs, and their classification and segmentation performances were assessed. A higher classification success was achieved for the ResNet-101 backbone pretrained on COCO images (%75 accuracy) in the testing phase. ResNet-50, on the other hand, achieved a higher classification accuracy (%87) when used with the ImageNet dataset. The classification and segmentation successes of meningioma and pituitary tumours were comparable, while glioma tumours could be segmented at relatively lower success rates (41–73% for all models), despite similar classification performances (73–95%) with other tumour types.

Orijinal dilİngilizce
Makale numarası2301391
DergiComputer Methods in Biomechanics and Biomedical Engineering: Imaging and Visualization
Hacim11
Basın numarası7
DOI'lar
Yayın durumuYayınlandı - 2024

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Publisher Copyright:
© 2024 Informa UK Limited, trading as Taylor & Francis Group.

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