Comparative study of convolutional neural network architectures for gastrointestinal lesions classification

Abstract

The gastrointestinal (GI) tract can be affected by different diseases or lesions such as esophagitis, ulcers, hemorrhoids, and polyps, among others. Some of them can be precursors of cancer such as polyps. Endoscopy is the standard procedure for the detection of these lesions. The main drawback of this procedure is that the diagnosis depends on the expertise of the doctor. This means that some important findings may be missed. In recent years, this problem has been addressed by deep learning (DL) techniques. Endoscopic studies use digital images. The most widely used DL technique for image processing is the convolutional neural network (CNN) due to its high accuracy for modeling complex phenomena. There are different CNNs that are characterized by their architecture. In this article, four architectures are compared: AlexNet, DenseNet-201, Inception-v3, and ResNet-101. To determine which architecture best classifies GI tract lesions, a set of metrics; accuracy, precision, sensitivity, specificity, F1-score, and area under the curve (AUC) were used. These architectures were trained and tested on the HyperKvasir dataset. From this dataset, a total of 6,792 images corresponding to 10 findings were used. A transfer learning approach and a data augmentation technique were applied. The best performing architecture was DenseNet-201, whose results were: 97.11% of accuracy, 96.3% sensitivity, 99.67% specificity, and 95% AUC.

A Performance Comparison of CNN Models for Bean Phenology Classification Using Transfer Learning Techniques

“…Using a re-trained CNN model has significant advantages in comparison to the design of models from zero, which require large sets of data and training that can take considerable time, including weeks, translating into high computational costs. On the other hand, a re-trained model can have a high capacity for generalization and accelerate convergence [34].…”

“…Using a re-trained CNN model has significant advantages in comparison to the design of models from zero, which require large sets of data and training that can take considerable time, including weeks, translating into high computational costs. On the other hand, a re-trained model can have a high capacity for generalization and accelerate convergence [34]. Recently, the scientific community has taken a particular interest in the transfer learning approach in diverse fields, such as medicine and agriculture, among others [28][29][30][31].…”

“…Most images have a resolution of 5120 × 3840 pixels. However, the images were re-dimensioned to adjust the size according to the entry specifications for each proposed model [34].…”