Classification-of-Anomalies-in-Gastrointestinal-Tract-through-Endoscopic-Imagery-with-Deep-Learning
We used pre-trained CNN for transfer learning to extract important features because this is relatively small dataset (only 8000 images). The pretrained models that we used in this approach are ResNet-50 and VGG-19. The proposed approach of anomalies classification is based on the steps as described below. First, we preprocessed the dataset due to some reasons as described in IV-B. Then we used pretrained CNNs and added global average pooling (GAP) layer to both and then combined the extracted features (2048 from ResNet50 and 512 from VGG19). After we added more dense layers and dropout regularization (dropout value as 0.5) to reduce overfitting and finally added SoftMax layer to identify the most matching category of an image. This model was trained with Adam optimization and we used mini batch size as 64 for this CNN. We used popular categorical cross-entropy loss function as the loss function of this mode. After 15 epochs, about 4 hours of training, our CNN achieved over 94% accuracy on train dataset and over 80% accuracy on test dataset.
