Impact of Image Resolution on Deep Learning Performance in Endoscopy Image Classification: An Experimental Study Using a Large Dataset of Endoscopic Images.

Vajira Thambawita,Pål Halvorsen,Steven A Hicks,Sravanthi Parasa,Inga Strümke,Michael A Riegler

doi:10.3390/diagnostics11122183

Abstract

Recent trials have evaluated the efficacy of deep convolutional neural network (CNN)-based AI systems to improve lesion detection and characterization in endoscopy. Impressive results are achieved, but many medical studies use a very small image resolution to save computing resources at the cost of losing details. Today, no conventions between resolution and performance exist, and monitoring the performance of various CNN architectures as a function of image resolution provides insights into how subtleties of different lesions on endoscopy affect performance. This can help set standards for image or video characteristics for future CNN-based models in gastrointestinal (GI) endoscopy. This study examines the performance of CNNs on the HyperKvasir dataset, consisting of 10,662 images from 23 different findings. We evaluate two CNN models for endoscopic image classification under quality distortions with image resolutions ranging from 32 × 32 to 512 × 512 pixels. The performance is evaluated using two-fold cross-validation and F1-score, maximum Matthews correlation coefficient (MCC), precision, and sensitivity as metrics. Increased performance was observed with higher image resolution for all findings in the dataset. MCC was achieved at image resolutions between 512 × 512 pixels for classification for the entire dataset after including all subclasses. The highest performance was observed with an MCC value of 0.9002 when the models were trained on the highest resolution and tested on the same resolution. Different resolutions and their effect on CNNs are explored. We show that image resolution has a clear influence on the performance which calls for standards in the field in the future.

Highlights

Research communities have put great efforts towards the automation of computeraided diagnostic tools with the ability to detect and classify a variety of different endoscopy findings
The performance of the convolutional neural network (CNN) is reported in terms of precision, sensitivity, F1-score, and Matthews correlation coefficient (MCC) for the DensNet-161 and ResNet-152 models
We propose a methodology to evaluate the effect of image resolution on the performance of CNN-based image classification by using a standard image dataset HyperKvasir

Summary

Introduction

Research communities have put great efforts towards the automation of computeraided diagnostic tools with the ability to detect and classify a variety of different endoscopy findings. One can see a large variation from high quality images to low quality ones in real world applications This depends on the equipment: for example, newer generations of smartphones take high quality and resolution pictures, whereas images in medical fields often cannot be assumed to be of high quality (due to old equipment, software, or lack of storage space for high quality data). Image quality factors, such as resolution, noise, contrast, blur, and compression, affect the visual information contained in the images [6]. The immediate visual information does not necessarily vary significantly, the details preserved in the visual information (e.g., fine vessels, the structure of the polyp surface) can vary drastically with the reduction of image resolution

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