Capsule Networks for Brain Tumor Classification Based on MRI Images and Coarse Tumor Boundaries

By Parnian Afshar, K. Plataniotis, Arash Mohammadi. IEEE International Conference on Acoustics, Speech, and Signal Processing, 2018

338 citations recorded by the source corpus.

According to official statistics, cancer is considered as the second leading cause of human fatalities. Among different types of cancer, brain tumor is seen as one of the deadliest forms due to its aggressive nature, heterogeneous characteristics, and low relative survival rate. Determining the type of brain tumor has significant impact on the treatment choice and patient’s survival. Human-centered diagnosis is typically error-prone and unreliable resulting in a recent surge of interest to automatize this process using convolutional neural networks (CNNs). CNNs, however, fail to fully utilize spatial relations, which is particularly harmful for tumor classification, as the relation between the tumor and its surrounding tissue is a critical indicator of the tumor’s type. In our recent work, we have incorporated newly developed CapsNets to overcome this shortcoming. CapsNets are, however, highly sensitive to the miscellaneous image background. The paper addresses this gap. The main contribution is to equip CapsNet with access to the tumor surrounding tissues, without distracting it from the main target. A modified CapsNet architecture is, therefore, proposed for brain tumor classification, which takes the tumor coarse boundaries as extra inputs within its pipeline to increase the CapsNet’s focus. The proposed approach noticeably outperforms its counterparts.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Neurology

View Paper: https://www.semanticscholar.org/paper/27f738236ed6612ee4ce0c5cdfa6f2ccae105c33

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Citations in the corpus

Classification of Brain Tumors from MRI Images Using a Convolutional Neural Network (2020, methodology): “In the literature, there are also studies that used the same database for classification with pre-trained networks [23,35–40] or, as input, they use only tumor region or some features that are extracted from the tumor region [7,21,23,41,42].”
Multi-Classification of Brain Tumor Images Using Deep Neural Network (2019, methodology): “[26] presented a capsule network (CapsNet) that integrates both theMRI brain image and the coarse tumor boundaries to classify the brain tumor.”
Deep CNN for Brain Tumor Classification (2021, methodology): “…No 989 (axial only) 90.26 Tahir et al. [29] No 3064 86 Afshar et al. [34] Both 3064 86.56 using segmentation 72.13 using raw images Afshar et al. [70] Bounding box 3064 90.89 Cheng et al. [27] Yes 3064 91.28 Ismael et al. [28] Yes 3064 91.9 Zhou et al. [35] No 989 (axial only) 92.13 Ghassemi et…”
Deep convolutional neural network based medical image classification for disease diagnosis (2019, methodology): “To test the Capsule network, a python capsule network implementation that aims to detect brain tumors was ported to the pneumonia dataset [37].”