A Dilated Convolutional Approach for Inflammatory Lesion Detection Using Multi-Scale Input Feature Fusion (Workshop Paper)
Samarjeet Kaur, Nidhi Goel
Abstract
The present manuscript proposes a novel CNN architecture to detect inflammatory lesion abnormality in Wireless Capsule Endoscopy (WCE) images. Such images encompasses a wide range of lesions and hence early diagnosis can be of vital importance. The proposed model learns the collective features of various inflammatory lesion subgroups and aggregates that information to solve a binary classification problem by distinguishing between normal and abnormal frames. The proposed model has one primary and three secondary branches. The primary branch resembles a generic CNN model with convolution and max-pooling layers whereas the secondary branches consist of dilated convolution layers and max-pooling layers. The proposed model fuses the multi-scale input context at varying dilation rates with different levels of the primary branch. This enhances feature quality by merging dominant global features with the local input context at multiple scales without any loss of resolution. The performance of the proposed model has been assessed using various objective evaluation metrics. The preliminary experiments indicates that the proposed model outperforms state-of-the-art models and exhibits an accuracy of 97.9%, sensitivity of 96%, specificity of 99%, ROC-AUC of 1 and Precision recall AUC of 99.7%.