CapsNet topology to classify tumours from brain images and comparative evaluation

GÖÇERİ E.

IET IMAGE PROCESSING, cilt.14, sa.5, ss.882-889, 2020 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 14 Sayı: 5
  • Basım Tarihi: 2020
  • Doi Numarası: 10.1049/iet-ipr.2019.0312
  • Dergi Adı: IET IMAGE PROCESSING
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, PASCAL, Aerospace Database, Applied Science & Technology Source, Business Source Elite, Business Source Premier, Communication Abstracts, Compendex, Computer & Applied Sciences, INSPEC, Metadex, Civil Engineering Abstracts
  • Sayfa Sayıları: ss.882-889
  • Anahtar Kelimeler: medical image processing, image classification, neural nets, feature extraction, brain, biomedical MRI, gradient methods, learning (artificial intelligence), expectation-maximisation algorithm, tumours, optimisation, CapsNet topology, brain images, brain tissues, meningioma, ependymoma, computer-assisted brain tumour classification techniques, Capsule-based neural networks, tumour recognition, brain magnetic resonance images, glioma, CapsNet based methods, expectation-maximisation based dynamic routing, tumour boundary information, pituitary, Sobolev gradient-based optimisation, network topology, feature extraction, learned features, QUALITY-OF-LIFE, PITUITARY-TUMORS, CLASSIFICATION, SEGMENTATION, TEXTURE, SURGERY, GLIOMAS
  • Akdeniz Üniversitesi Adresli: Evet

Özet

Visual evaluation of many magnetic resonance images is a difficult task. Therefore, computer-assisted brain tumor classification techniques have been proposed. These techniques have several drawbacks or limitations. Capsule based neural networks are new approaches that can preserve spatial relationships of learned features using dynamic routing algorithm. By this way, not only performance of tumor recognition increases but also sampling efficiency and generalisation capability improves. Therefore, in this work, a Capsule Network (CapsNet) is used to achieve fully automated classification of tumors from brain magnetic resonance images. In this work, prevalent three types of tumors (pituitary, glioma and meningioma) have been handled. The main contributions in this paper are as follows: 1) A comprehensive review on CapsNet based methods is presented. 2) A new CapsNet topology is designed by using a Sobolev gradient-based optimisation, expectation-maximisation based dynamic routing and tumor boundary information. 3) The network topology is applied to categorise three types of brain tumors. 4) Comparative evaluations of the results obtained by other methods are performed. According to the experimental results, the proposed CapsNet based technique can achieve extraction of desired features from image data sets and provides tumor classification automatically with 92.65% accuracy.