A Dilated Convolutional Approach for Inflammatory Lesion Detection Using Multi-Scale Input Feature Fusion

EasyChair Preprint 4421

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 lies 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 with an accuracy of 97.9%, ROC-AUC of $1$ and Precision recall AUC of 99.7.

Keyphrases: Convolution Neural Network, Wireless capsule endoscopy, lesion detection

@booklet{EasyChair:4421,
  author    = {Samarjeet Kaur and Nidhi Goel},
  title     = {A Dilated Convolutional Approach for Inflammatory Lesion Detection Using Multi-Scale Input Feature Fusion},
  howpublished = {EasyChair Preprint 4421},
  year      = {EasyChair, 2020}}