Capsule Networks for Brain Tumor Classification based on MRI Images and Course Tumor Boundaries
This work addresses the problem of accurate brain tumor classification for medical diagnosis, which is crucial for treatment planning, but it appears incremental as it builds on existing CapsNet methods with a specific modification.
The paper tackles brain tumor classification from MRI images by addressing Capsule Networks' sensitivity to background noise, proposing a modified architecture that incorporates coarse tumor boundaries to improve focus on relevant tissues. The approach significantly outperforms existing methods, though specific performance numbers are not provided.
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.