Capsule Vision Challenge - A deep learning solution using ResNetSE

This study presents an approach to classify abnormalities in video capsule endoscopy

(VCE) frames using a modified ResNet101 architecture with Squeeze and Excitation (SE)

blocks. The aim was to build a generalized model capable of automatic abnormality

detection across ten classes namely Angioectasia, Bleeding, Erosion, Erythema, Foreign

Body, Lymphangiectasia, Polyp, Ulcer, Worms, and Normal. Our approach involves

augmenting and balancing the dataset to address class imbalance, followed by training

and evaluating the model. The model achieved a mean AUC of 0.984, mean specificity of

0.990, mean average precision of 0.839, mean sensitivity of 0.759, and a balanced accuracy

of 0.780. These results demonstrate the potential of SE-ResNet101 for VCE abnormality

detection, with opportunities for further improvement in sensitivity and overall accuracy.