Edge Computing Using Embedded Webserver with Mobile Device for Diagnosis and Prediction of Metastasis in Histopathological Images

Anil Johny,K N Madhusoodanan

doi:10.1007/s44196-021-00040-x

Abstract

Diagnosis of different breast cancer stages using histopathology whole slide images is the gold standard in grading the tissue metastasis. Traditional diagnosis involves labor intensive procedures and is prone to human errors. Computer aided diagnosis assists medical experts as a second opinion tool in early detection which prevents further proliferation. Computing facilities have emerged to an extent where algorithms can attain near human accuracy in prediction of diseases, offering better treatment to curb further proliferation. The work introduced in the paper provides an interface in mobile platform, which enables the user to input histopathology image and obtain the prediction results with its class probability through embedded web-server. The trained deep convolutional neural networks model is deployed into a microcomputer-based embedded system after hyper-parameter tuning, offering congruent performance. The implementation results show that the embedded platform with custom-trained CNN model is suitable for medical image classification, as it takes less execution time and mean prediction time. It is also noticed that customized CNN classifier model outperforms pre-trained models when used in embedded platforms for prediction and classification of histopathology images. This work also emphasizes the relevance of portable and flexible embedded device in real time clinical applications.

Highlights

There are millions of embedded devices connected to the internet today and it is increasing day by day and total devices connected will reach nearly 5.3 billion by 2023 [1]
This paper proposes a deep learning approach for malignancy detection in histopathology images using limited resources of embedded platform
Embedded devices with limited computing resources can be used for prediction and classification of histopathology images by tweaking trained deep Convolutional Neural networks (CNNs) model into the device

Summary

Introduction

There are millions of embedded devices connected to the internet today and it is increasing day by day and total devices connected will reach nearly 5.3 billion by 2023 [1]. The number of devices connected to internet are huge so that the network handles enormous amount of data every day. Due to rising number of digitally connected devices, internet traffic will reach 20.6 zeta-bytes (ZB) [1]. The model training with dataset for specific application can be done with the help of cloud or any computing device with GPU support to reduce the training time. The model inferences can be done inside embedded platform as the process requires less computing resource. Machine learning modules can be incorporated in low resource end-devices but deep learning requires computational power and memory. Such applications are computation resource hungry, so they are executed mostly on cloud servers or personal computers with reasonable hardware

Methods

Results

Conclusion