DCDS-Net: Deep transfer network based on depth-wise separable convolution with residual connection for diagnosing gastrointestinal diseases

Abstract

Gastrointestinal (GI) diseases are the most common in the human digestive system and has a significantly higher mortality rate. Accurate evaluation of endoscopic images plays an important role in decision making regarding patient treatment. Recently, convolutional neural networks (CNNs) have been introduced for the diagnosis of GI diseases. However, achieving high accuracy is still a challenging task. To overcome these limitations, we propose the “Densely Connected Depth-wise Separable Convolution-Based Network” (DCDS-Net) model, utilizing depth-wise separable convolution (DWSC) with residual connections and densely connected blocks (DCB), to effectively diagnose various endoscopic images of GI diseases. In addition, we incorporate global average pooling (GAP), batch normalization, dropout and dense layers in DCB to learn rich discriminative features and improve the performance of the model. We explored the feasibility of block-wise fine-tuning using transfer learning on the proposed model to reduce overfitting, and experimentally explore the optimal level of fine-tuning, since transfer learning is well suited to medical data where labeled data is scarce. The proposed method has been evaluated on 6000 labeled endoscopic images containing 4 classes of GI diseases. In addition, data augmentation has been incorporated into the training pipeline to improve the performance of the model. Furthermore, a critical study was conducted to evaluate the generalizability of the proposed model on smaller training samples (e.g., 60 %, 70 %, 80 %, and 90 %). The study employed Grad-CAM to generate heatmaps that identify the regions in the GI tract that are indicative of the presence of different diseases. The results of extensive experiments show that the proposed model shows significant improvements and achieves the highest classification accuracy of 99.33 %, precision of 99.37 %, recall of 99.32 % and outperforms all pre-trained and existing models for the detection of GI diseases. In conclusion, DCDS-Net exhibits high classification performance and can help endoscopists in automatic GI disease diagnosis.