Dense-Res Net for Endoscopic Image Classification

We propose a method that configures Fine-tuning to a combination of backbone DenseNet and ResNet to classify eight classes showing anatomical landmarks, pathological findings, to endoscopic procedures in the GI tract. Our Technique depends on Transfer Learning which combines two backbones, DenseNet 121 and ResNet 101, to improve the performance of Feature Extraction for classifying the target class. After experiment and evaluating our work, we get accuracy with an F1 score of approximately 0.93 while training 80000 and test 4000 images.