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.

翻译：本文总结了MISAHUB团队在2024年胶囊视觉挑战赛中的研究工作。针对胶囊视觉挑战数据集因复杂性和类别不平衡而极具挑战性的多类疾病分类任务，本文提出了一种参数高效的新型模型CASCRNet（胶囊内窥镜-ASPP-SCR网络），该模型采用了共享通道残差（SCR）模块与空洞空间金字塔池化（ASPP）模块。此外，本文将所提模型的性能与其他知名方法进行了比较。实验结果表明，所提模型能提供更优的疾病分类结果。该模型在紧凑的架构下，成功实现了疾病分类，其F1分数达到78.5%，平均AUC为98.3%，性能表现优异。