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

Capsule Networks for Brain Tumor Classification based on MRI Images and Course Tumor Boundaries