  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.
