  Video capsule endoscopy is a hot topic in computer vision and medicine. Deep
learning can have a positive impact on the future of video capsule endoscopy
technology. It can improve the anomaly detection rate, reduce physicians' time
for screening, and aid in real-world clinical analysis. CADx classification
system for video capsule endoscopy has shown a great promise for further
improvement. For example, detection of cancerous polyp and bleeding can lead to
swift medical response and improve the survival rate of the patients. To this
end, an automated CADx system must have high throughput and decent accuracy. In
this paper, we propose FocalConvNet, a focal modulation network integrated with
lightweight convolutional layers for the classification of small bowel
anatomical landmarks and luminal findings. FocalConvNet leverages focal
modulation to attain global context and allows global-local spatial
interactions throughout the forward pass. Moreover, the convolutional block
with its intrinsic inductive/learning bias and capacity to extract hierarchical
features allows our FocalConvNet to achieve favourable results with high
throughput. We compare our FocalConvNet with other SOTA on Kvasir-Capsule, a
large-scale VCE dataset with 44,228 frames with 13 classes of different
anomalies. Our proposed method achieves the weighted F1-score, recall and MCC}
of 0.6734, 0.6373 and 0.2974, respectively outperforming other SOTA
methodologies. Furthermore, we report the highest throughput of 148.02
images/second rate to establish the potential of FocalConvNet in a real-time
clinical environment. The code of the proposed FocalConvNet is available at
https://github.com/NoviceMAn-prog/FocalConvNet.
