A Novel Integrated Convolutional Neural Network via Deep Transfer Learning in Colorectal Images

Abstract

In this paper, we explore the use of current deep learning methods, convolutional neural networks (CNNs) in the field of computer-aided diagnosis systems to classify several endoscopic colon diseases. Transfer learning by fine-tuning deep convolutional neural networks (CNNs) is applied due to the limited amount of data. For this, state-of-the-art CNN architectures, such as VGG16, VGG19, InceptionV3, ResNet50, Inception-ResNet-V2, DenseNet169 were used for training and validating the dataset. However, these existing architectures cannot extract more dense endoscopic image features and have problem on similar-looking images of different category. Therefore, we propose a novel integrated convolutional neural network to develop a more accurate and highly efficient method for endoscopic image classification, which uses the features of earlier layers in the classification process and increases the receptive field of view at the end layers in the network. We compare and evaluate our performance using performance metrics Accuracy (ACC), Recall, Precision and F1-score. In our experimental results, the proposed method outperforms the existing architectures, obtaining an accuracy of about 92.4% on the test dataset.