Capsule Networks for Brain Tumor Classification based on MRI Images and Course Tumor Boundaries (1811.00597v1)

Abstract: 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.

• The paper proposes a modified Capsule Network (CapsNet) that uses full MRI images and coarse tumor boundaries for brain tumor classification, addressing spatial information loss in traditional methods.
• Their approach achieved 90.89% accuracy, outperforming traditional CNNs and standard CapsNets without boundary data.
• Integrating coarse boundaries improves efficiency and practicality for clinical use by reducing the need for detailed tumor segmentation.

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

This paper addresses the challenges of brain tumor classification using MRI images by leveraging Capsule Networks (CapsNets), an innovative approach in deep learning. It focuses on overcoming the limitations of Convolutional Neural Networks (CNNs), which while widely used, tend to neglect spatial relationships crucial for accurate tumor classification.

Motivation and Approach

Brain tumors remain one of the most lethal forms of cancer due to their aggressive and heterogeneous nature. The precise classification of brain tumor types is critical for determining treatment strategies and improving patient survival rates. Traditional diagnostic methods rely heavily on radiologists, whose assessments can be subjective and prone to error.

CNNs, although a popular choice for image classification tasks, suffer from shortcomings related to spatial information loss due to pooling layers, which prevent them from fully capturing the spatial hierarchies in image data. This inadequacy is particularly problematic in medical imaging, where the spatial relationships between a tumor and its surrounding tissue can provide vital diagnostic clues.

Capsule Networks, introduced by Hinton et al., offer an alternative by preserving spatial hierarchies and improving resistance to transformations and variability in image orientation. Each capsule is a group of neurons representing both the presence of a feature and its instantiation parameters, which allows CapsNets to maintain the spatial relationships between the parts of an object.

The principal contribution of this paper is the design of a modified CapsNet architecture, which, unlike traditional approaches requiring segmented tumors for input, utilizes full MRI images alongside coarse tumor boundary information. This method enables the CapsNet to focus on relevant regions without the need for detailed and labor-intensive manual annotation, thus balancing focus and efficiency.

Key Contributions and Findings

The proposed approach integrates tumor boundary data directly into the CapsNet pipeline by attaching it to the instantiation vectors of capsule outputs. This enables the network to consider the contextual information provided by surrounding tissues while maintaining a focus on the tumor itself.

Key results are detailed as follows:

• The CapsNet variant incorporating both MRI images and tumor boundaries achieved an accuracy of 90.89%, surpassing traditional CNN architectures and standalone CapsNets that operated without boundary data.
• By reducing reliance on detailed tumor segmentation, the approach improves efficiency, making it a practical alternative in clinical settings where resources are constrained.
• The findings underscore the robustness of CapsNets in medical image processing, particularly in scenarios requiring high sensitivity to spatial dynamics.

Implications and Future Directions

This paper demonstrates a significant step forward in automating brain tumor classification, providing a viable pathway for clinical adoption where precision and turnaround time are paramount. The results gleaned from this paper can inform both the practical deployment of imaging solutions in healthcare and future research focusing on model interpretability and robustness under diverse imaging conditions.

The work suggests fertile avenues for future exploration, particularly in enhancing CapsNet interpretability. Given the medical community's demand for transparent machine learning models capable of elucidating decision pathways, there is a need for ongoing research into making capsule outputs as informative and actionable as possible.

Lastly, considering the paper's context within the broader sphere of AI applications, these findings point toward the evolutionary trajectory of machine learning models away from black-box paradigms towards systems that are both high-performing and comprehensible. The advancements discussed lay groundwork for the development of smarter, more context-sensitive AI paradigms that hold great promise not just for medical imaging, but for any domain where complex pattern recognition is crucial.