Use of Neural Network-Based Deep Learning Techniques for the Diagnostics of Skin Diseases

Gavrilov, Daniil; Melerzanov, Alexander; Shchelkunov, N. N.; Закиров, Э. И.

doi:10.1007/s10527-019-09845-9

Cited by 25 publications

(15 citation statements)

References 2 publications

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“…Gavrilov et al [175] proposed a skin neoplasm (cancer) classification system using CNN. They applied transfer learning to inception V3 (Googlenet).…”

Section: Deep Learningmentioning

confidence: 99%

Machine Learning and Deep Learning Methods for Skin Lesion Classification and Diagnosis: A Systematic Review

et al. 2021

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Computer-aided systems for skin lesion diagnosis is a growing area of research. Recently, researchers have shown an increasing interest in developing computer-aided diagnosis systems. This paper aims to review, synthesize and evaluate the quality of evidence for the diagnostic accuracy of computer-aided systems. This study discusses the papers published in the last five years in ScienceDirect, IEEE, and SpringerLink databases. It includes 53 articles using traditional machine learning methods and 49 articles using deep learning methods. The studies are compared based on their contributions, the methods used and the achieved results. The work identified the main challenges of evaluating skin lesion segmentation and classification methods such as small datasets, ad hoc image selection and racial bias.

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“…Gavrilov et al [175] proposed a skin neoplasm (cancer) classification system using CNN. They applied transfer learning to inception V3 (Googlenet).…”

Section: Deep Learningmentioning

confidence: 99%

Machine Learning and Deep Learning Methods for Skin Lesion Classification and Diagnosis: A Systematic Review

et al. 2021

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“…Several research studies have been proposed in the literature aiming to improve the accuracy of skin diagnosis [63][64][65][66][67][68][69][70][71][72]. Convolutional neural networks (CNN) are adopted in most proposals [63][64][65][66][67][68][69][70][71], except in [72] where the authors proposed fuzzy classification for skin lesion segmentation. Some proposals have considered other information or data in the diagnosis process such as demographic and medical history [66] and sonification (audio) [73].…”

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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“…The outcome of such a hybrid system shows that the effects of classification were substantially improved. Gavrilov et al [51] proposed an early diagnostic algorithm focused on deep convolutional neural networks which efficiently differentiate between benign and malignant skin cancer. Mahbod et al [53] provide a fully automatic computerized system for the classification of skin lesions that uses optimized deep features from a range of wellestablished pre-trained CNN models such as AlexNet, VGG16 and ResNet-18 and then trains vector machine classifiers.…”

Section: A) Pre-trained Modelsmentioning

confidence: 99%

Malignant Melanoma Classification Using Deep Learning: Datasets, Performance Measurements, Challenges and Opportunities

et al. 2020

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Melanoma remains the most harmful form of skin cancer. Convolutional neural network (CNN) based classifiers have become the best choice for melanoma detection in the recent era. The research has indicated that classifiers based on CNN classify skin cancer images equivalent to dermatologists, which has allowed a quick and life-saving diagnosis. This study provides a systematic literature review of the latest research on melanoma classification using CNN. We restrict our study to the binary classification of melanoma. In particular, this research discusses the CNN classifiers and compares the accuracies of these classifiers when tested on non-published datasets. We conducted a systematic review of existing literature, identifying the literature through a systematic search of the IEEE, Medline, ACM, Springer, Elsevier, and Wiley databases. A total of 5112 studies were identified out of which 55 well-reputed studies were selected. The main objective of this study is to collect state of the art research which identify the recent research trends, challenges and opportunities for melanoma diagnosis and investigate the existing solutions for the diagnosis of melanoma detection using deep learning. Moreover, proposed taxonomy for melanoma detection has been presented that summarizes the broad variety of existing melanoma detection solutions. Lastly, proposed model, challenges and opportunities have been presented which helps the researchers in the domain of melanoma detection.

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Use of Neural Network-Based Deep Learning Techniques for the Diagnostics of Skin Diseases

Cited by 25 publications

References 2 publications

Machine Learning and Deep Learning Methods for Skin Lesion Classification and Diagnosis: A Systematic Review

Machine Learning and Deep Learning Methods for Skin Lesion Classification and Diagnosis: A Systematic Review

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

Malignant Melanoma Classification Using Deep Learning: Datasets, Performance Measurements, Challenges and Opportunities

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