The Value of Digital Dermatoscopy in the Diagnosis and Treatment of Precancerous Skin Lesions

Claudiu, Podac; Bordeianu, I.; Tudorel, Poalelungi; Iordache, Ionut-Eduard; Nida, Bosnac; Abdulazis, T.; Panculescu, F.G.

doi:10.2478/arsm-2018-0008

Cited by 2 publications

(1 citation statement)

References 12 publications

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“…This helps professionals to detect the earliest signs of abnormality and support their evaluation. Clinical imaging and dermatoscopy are now considered to be an essential part of the dermatology clinics for diagnosis, treatment, follow-up, and documentation [60,61]. Skin diagnosis (and identifying benign and malignant skin lesions) is an important factor in the early detection and prevention of skin cancer.…”

Section: Skin Lesion Diagnosismentioning

confidence: 99%

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

Janbi

Mehmood

Katib

et al. 2022

Sensors

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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.

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Section: Skin Lesion Diagnosismentioning

confidence: 99%