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

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.

Real-world Machine Learning Systems: A survey from a Data-Oriented Architecture Perspective

“…Some of the components of these systems act as independent entities that interact toward a goal. They can act as sensors that transmit data to cloud servers [16,48,55,58,62,86,101] or systems' subcomponents that hide their own data model [47,67,98,99,111].…”

“…For example, systems can use API calls to collect data from data sources [10,81] or to receive requests from and send responses to end users [49] reviewed papers (i.e., 56.5%) do not adopt data coupling as data is hidden by each systems' components and these components interact using traditional process calls. They can use API calls [14,31,48,55,62,67,75,86,99,102,109] or remote procedure calls (RPC) [8,15,16,29,43,47,57,58,98,101,103,111,123,126] for communication. We found that not all the systems where components share a common data model necessarily follow the data coupling sub-principle.…”

“…Some of these systems are deployed on single cloud servers [10,31,47,49,56,81,98,105], where all data is stored. Other systems are deployed on resource-constraint devices like robots [57,67,106], or small processing units (e.g., Raspberry Pis) [23,43,62]. While cloud servers are flexible and can handle big-data requirements like the ones addressed by Habibi et al [56] or Schubert et al [105], resource-constraint devices limit the amount of stored data to the point where only trained learning models are deployed [62].…”

“…Other systems are deployed on resource-constraint devices like robots [57,67,106], or small processing units (e.g., Raspberry Pis) [23,43,62]. While cloud servers are flexible and can handle big-data requirements like the ones addressed by Habibi et al [56] or Schubert et al [105], resource-constraint devices limit the amount of stored data to the point where only trained learning models are deployed [62]. Finally, there are systems that partially distribute the storage of data (i.e., 19.6% of the papers).…”

Deep learning pipeline for image classification on mobile phones

“…This article [17] discusses the various challenges that arise when deploying a machine learning model on mobile phones. This work [18] employs edge computing on a mobile device with an integrated web server to diagnose and forecast metastasis in histopathology pictures.…”