A comparitive analysis of mobilenet and xception architecture for classification of endoscopy images

Abstract

The deep architectures have been making their significant mark in solving the image classification problems. The usage of the standard deep learning architectures has been increasing at a greater pace because of the outcome of the state-of-the art accuracy and precision by incorporating these architectures in image classification problems. The classification of medical images from variety of modalities is also possible with better accuracy by using these deep architectures. The standard architectures that have achieved substantial results in general object classification can be used and their learning can be transferred to the required domain. Further, these architectures can be used in an ensemble structure thereby creating a customized architecture for solving the classification problem. Some of the challenges in including these architectures include, the enormous memory and processing power requirement. Current trends are focused towards generating optimized deep structures that has relatively lesser memory and processing capacity requirement. To this end, the two benchmark architectures that have obtained momentous results in object classification namely the MobileNetV2 and Xception are included in this study to perform binary classification of capsule endoscopy images to detect the presence of abnormality. Capsule endoscopy (CE) is a medical procedure where the patient swallows the pill-based camera that traverses the entire gastrointestinal (GI) path capturing numerous images. These images are to be assessed for detecting the presence of any abnormality. The architectures included in this work have been trained on the images from the publicly available CE datasets and an ablation study on the hyperparameter tuning of the architecture have been conducted. The results are compared, analyzed and presented here. The MobileNetV2 and Xception architecture have achieved a maximum accuracy of 85% and 82% in the abnormality detection in CE images.