A new approach for classification skin lesion based on transfer learning, deep learning, and IoT system

Rodrigues, Douglas de A.; Ivo, Roberto F.; Satapathy, Suresh Chandra; Wang, Shuihua; Hemanth, D. Jude; Filho, Pedro Pedrosa Rebouças

doi:10.1016/j.patrec.2020.05.019

Cited by 105 publications

(47 citation statements)

References 10 publications

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“…In the medical domain, automated disease classification plays an important role during the mass data assessment and a perfectly tuned disease classification system further reduces the diagnostic burden of physicians and acts as an assisting system during the decision-making process [ 32 , 33 , 34 , 35 ]. Therefore, a considerable number of disease detection systems assisted by DL are proposed and implemented in the literature [ 36 , 37 , 38 , 39 , 40 ]. Recent DL schemes implemented in the LIDC-IDRI with fused deep and HCF helped achieve a classification accuracy of >97% [ 13 ].…”

Section: Methodsmentioning

confidence: 99%

VGG19 Network Assisted Joint Segmentation and Classification of Lung Nodules in CT Images

et al. 2021

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Pulmonary nodule is one of the lung diseases and its early diagnosis and treatment are essential to cure the patient. This paper introduces a deep learning framework to support the automated detection of lung nodules in computed tomography (CT) images. The proposed framework employs VGG-SegNet supported nodule mining and pre-trained DL-based classification to support automated lung nodule detection. The classification of lung CT images is implemented using the attained deep features, and then these features are serially concatenated with the handcrafted features, such as the Grey Level Co-Occurrence Matrix (GLCM), Local-Binary-Pattern (LBP) and Pyramid Histogram of Oriented Gradients (PHOG) to enhance the disease detection accuracy. The images used for experiments are collected from the LIDC-IDRI and Lung-PET-CT-Dx datasets. The experimental results attained show that the VGG19 architecture with concatenated deep and handcrafted features can achieve an accuracy of 97.83% with the SVM-RBF classifier.

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Section: Methodsmentioning

confidence: 99%

VGG19 Network Assisted Joint Segmentation and Classification of Lung Nodules in CT Images

et al. 2021

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“…Another potential application is the detection of lung cancer using various state-of-the-art machine learning algorithms with an IoT-based system [131][132][133]. Moreover, a recent piece of research also suggested the detection of skin lesions using an IoTbased system [134]. Cecil et al have employed IoT in designing the next-generation surgical training framework [135].…”

Section: Other Notable Applicationsmentioning

confidence: 99%

IoT-Based Applications in Healthcare Devices

Pradhan

Bhattacharyya

Pal

2021

Journal of Healthcare Engineering

258

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The last decade has witnessed extensive research in the field of healthcare services and their technological upgradation. To be more specific, the Internet of Things (IoT) has shown potential application in connecting various medical devices, sensors, and healthcare professionals to provide quality medical services in a remote location. This has improved patient safety, reduced healthcare costs, enhanced the accessibility of healthcare services, and increased operational efficiency in the healthcare industry. The current study gives an up-to-date summary of the potential healthcare applications of IoT- (HIoT-) based technologies. Herein, the advancement of the application of the HIoT has been reported from the perspective of enabling technologies, healthcare services, and applications in solving various healthcare issues. Moreover, potential challenges and issues in the HIoT system are also discussed. In sum, the current study provides a comprehensive source of information regarding the different fields of application of HIoT intending to help future researchers, who have the interest to work and make advancements in the field to gain insight into the topic.

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“…Deep learning (DL) algorithms are considered the last important breakthrough in CAD as they are the evolution of conventional ML and state-of-the-art techniques, since most of the literature corresponds to the last two years. The vast majority of DL approaches are based on neural networks, and while some of them are applied to the whole computational diagnosis process (lesion segmentation, feature extraction, and classification) [ 126 , 127 , 128 , 129 ], others are used in combination with traditional ML classifiers such as SVM, KNN, or RF [ 130 , 131 , 132 , 133 ]. In general terms, DL consists of a network of multiple hidden layers where each of them is connected non-linearly to other hidden layers ( Figure 10 ) and becomes accurately weighted when an optimization routine is applied [ 130 ].…”

Section: Learning Algorithms For Skin Cancer Diagnosismentioning

confidence: 99%

Optical Technologies for the Improvement of Skin Cancer Diagnosis: A Review

Rey-Barroso

Peña-Gutiérrez

Yáñez

et al. 2021

Sensors

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The worldwide incidence of skin cancer has risen rapidly in the last decades, becoming one in three cancers nowadays. Currently, a person has a 4% chance of developing melanoma, the most aggressive form of skin cancer, which causes the greatest number of deaths. In the context of increasing incidence and mortality, skin cancer bears a heavy health and economic burden. Nevertheless, the 5-year survival rate for people with skin cancer significantly improves if the disease is detected and treated early. Accordingly, large research efforts have been devoted to achieve early detection and better understanding of the disease, with the aim of reversing the progressive trend of rising incidence and mortality, especially regarding melanoma. This paper reviews a variety of the optical modalities that have been used in the last years in order to improve non-invasive diagnosis of skin cancer, including confocal microscopy, multispectral imaging, three-dimensional topography, optical coherence tomography, polarimetry, self-mixing interferometry, and machine learning algorithms. The basics of each of these technologies together with the most relevant achievements obtained are described, as well as some of the obstacles still to be resolved and milestones to be met.

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A new approach for classification skin lesion based on transfer learning, deep learning, and IoT system

Cited by 105 publications

References 10 publications

VGG19 Network Assisted Joint Segmentation and Classification of Lung Nodules in CT Images

VGG19 Network Assisted Joint Segmentation and Classification of Lung Nodules in CT Images

IoT-Based Applications in Healthcare Devices

Optical Technologies for the Improvement of Skin Cancer Diagnosis: A Review

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