CASCRNet: An Atrous Spatial Pyramid Pooling and Shared Channel Residual based Network for Capsule Endoscopy

TL;DR

CASCRNet is a novel, parameter-efficient neural network that combines ASPP and SCR blocks to improve multi-class disease classification in capsule endoscopy images, achieving high accuracy and AUC.

Contribution

The paper introduces CASCRNet, a new model integrating ASPP and SCR blocks for capsule endoscopy classification, addressing dataset complexity and imbalance.

Findings

Achieved F1 Score of 78.5% in disease classification.

Obtained Mean AUC of 98.3%, demonstrating high discriminative ability.

Outperformed other approaches in the challenge.

Abstract

This manuscript summarizes work on the Capsule Vision Challenge 2024 by MISAHUB. To address the multi-class disease classification task, which is challenging due to the complexity and imbalance in the Capsule Vision challenge dataset, this paper proposes CASCRNet (Capsule endoscopy-Aspp-SCR-Network), a parameter-efficient and novel model that uses Shared Channel Residual (SCR) blocks and Atrous Spatial Pyramid Pooling (ASPP) blocks. Further, the performance of the proposed model is compared with other well-known approaches. The experimental results yield that proposed model provides better disease classification results. The proposed model was successful in classifying diseases with an F1 Score of 78.5% and a Mean AUC of 98.3%, which is promising given its compact architecture.