Imtidad: A Reference Architecture and a Case Study on Developing Distributed AI Services for Skin Disease Diagnosis over Cloud, Fog and Edge

Abstract

Several factors are motivating the development of preventive, personalized, connected, virtual, and ubiquitous healthcare services. These factors include declining public health, increase in chronic diseases, an ageing population, rising healthcare costs, the need to bring intelligence near the user for privacy, security, performance, and costs reasons, as well as COVID-19. Motivated by these drivers, this paper proposes, implements, and evaluates a reference architecture called Imtidad that provides Distributed Artificial Intelligence (AI) as a Service (DAIaaS) over cloud, fog, and edge using a service catalog case study containing 22 AI skin disease diagnosis services. These services belong to four service classes that are distinguished based on software platforms (containerized gRPC, gRPC, Android, and Android Nearby) and are executed on a range of hardware platforms (Google Cloud, HP Pavilion Laptop, NVIDIA Jetson nano, Raspberry Pi Model B, Samsung Galaxy S9, and Samsung Galaxy Note 4) and four network types (Fiber, Cellular, Wi-Fi, and Bluetooth). The AI models for the diagnosis include two standard Deep Neural Networks and two Tiny AI deep models to enable their execution at the edge, trained and tested using 10,015 real-life dermatoscopic images. The services are evaluated using several benchmarks including model service value, response time, energy consumption, and network transfer time. A DL service on a local smartphone provides the best service in terms of both energy and speed, followed by a Raspberry Pi edge device and a laptop in fog. The services are designed to enable different use cases, such as patient diagnosis at home or sending diagnosis requests to travelling medical professionals through a fog device or cloud. This is the pioneering work that provides a reference architecture and such a detailed implementation and treatment of DAIaaS services, and is also expected to have an extensive impact on developing smart distributed service infrastructures for healthcare and other sectors.

Keywords: TensorFlow; cloud computing; distributed AI as a service (DAIaaS); edge computing; fog computing; healthcare; reference architecture; skin disease diagnosis; smart cities; smart healthcare; smart societies; tiny AI; tiny ML.

Figure 1

The Imtidad reference architecture (a high-level view).

Figure 2

Imtidad reference architecture.

Figure 3

Workflow Diagram for creating a skin disease diagnosis catalog refined from Imtidad Reference Architecture.

Figure 4

NVIDIA Jetson Nano.

Figure 5

Raspberry Pi 4 Model B.

Figure 6

Samsung Galaxy Smartphones.

Figure 7

Imtidad testbed: devices and platforms.

Figure 8

System architecture and design (skin lesion diagnosis services).

Figure 9

Model (A) architecture.

Figure 10

Model (B) architecture.

Figure 11

The accuracy heatmap of different classes: (a) Model A; (b) Model B.

Figure 12

Mobile remote service nearby connection workflow.

Figure 13

Mobile application user interface for access to skin diagnosis services.

Figure 14

Networking setup.

Figure 15

Service processing time (by device).

Figure 16

Service processing time (by model).

Figure 17

Service response time (by device).

Figure 18

Service response time (by model).

Figure 19

Service network time (by device).

Figure 20

Service network time (by model).

Figure 21

Network time at different times and days.

Figure 22

Network time for fiber and 4G of the cloud services.

Figure 23

Service data transfer rate (by device).

Figure 24

Service data transfer rate (by model).

Figure 25

Service energy consumption.

Figure 26

Service eValue.

Figure 27

Service sValue.