Internet of Medical Things (IoMT) Enabled Skin Lesion Detection and Classification Using Optimal Segmentation and Restricted Boltzmann Machines

Anandaraj, A. Peter Soosai; Gomathy, V.; Punitha, A.; Kumari, Divya; Rani, S. Sheeba; Sureshkumar, S.

doi:10.1007/978-3-030-55833-8_12

Cited by 11 publications

(3 citation statements)

References 13 publications

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“…ABC has also been used for diagnosing and classifying skin lesions using optimal segmentation and limited Boltzmann machines [ 86 ]. The proposed model includes a set of steps such as image acquisition, segmentation, feature extraction, and classification.…”

Section: Application Of Si In Iot/iomtmentioning

confidence: 99%

Swarm Intelligence in Internet of Medical Things: A Review

Alizadehsani

Roshanzamir

Izadi

et al. 2023

Sensors

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Continuous advancements of technologies such as machine-to-machine interactions and big data analysis have led to the internet of things (IoT) making information sharing and smart decision-making possible using everyday devices. On the other hand, swarm intelligence (SI) algorithms seek to establish constructive interaction among agents regardless of their intelligence level. In SI algorithms, multiple individuals run simultaneously and possibly in a cooperative manner to address complex nonlinear problems. In this paper, the application of SI algorithms in IoT is investigated with a special focus on the internet of medical things (IoMT). The role of wearable devices in IoMT is briefly reviewed. Existing works on applications of SI in addressing IoMT problems are discussed. Possible problems include disease prediction, data encryption, missing values prediction, resource allocation, network routing, and hardware failure management. Finally, research perspectives and future trends are outlined.

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Section: Application Of Si In Iot/iomtmentioning

confidence: 99%

Swarm Intelligence in Internet of Medical Things: A Review

Alizadehsani

Roshanzamir

Izadi

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

“…Recent developments pave the establishment of a skin care and monitoring device called SkinAid, an IoMT pipeline, for skin lesions, which incorporates GAN-based data augmentation [ 56 ]. Similarly, numerous IoMT devices for skin lesions classification approaches have been developed without a clear explanation of how the device might be used in a home setting [ 57 , 58 ].…”

Section: Related Workmentioning

confidence: 99%

A Secure Framework toward IoMT‐Assisted Data Collection, Modeling, and Classification for Intelligent Dermatology Healthcare Services

Islam

Kaushal

Amin

et al. 2022

Contrast Media & Molecular Imaging

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The abnormal growth of the skin cells is known as skin cancer. It is one of the main problems in the dermatology area. Skin lesions or malignancies have been a source of worry for many individuals in recent years. Irrespective of the skin tone, there exist three major classes of skin lesions, i.e., basal cell carcinoma, squamous cell carcinoma, and melanoma. The early diagnosis of these lesions is equally important for human life. In the proposed work, a secure IoMT-Assisted framework is introduced that can help the patients to do the initial screening of skin lesions remotely. The initially proposed approach uses an IoMT-based data collection device which is accessible by patients to capture skin lesions images. Next, the captured skin sample is encrypted and sent to the collected image toward cloud storage. Later, the received sample image is classified into appropriate class labels using an ensemble classifier. In the proposed framework, four CNN models were ensemble i.e., VGG-16, DenseNet-201, Inception-V3, and Efficient-B7. The framework has experimented with the “HAM10000” dataset having 7 different kinds of skin lesions data. Although DenseNet-201 performed well, the ensemble model provides the highest accuracy with 87.22 percent as well as its test loss/error is lower than others with 0.4131. Moreover, the ensemble model’s classification ability is much higher with an AUC score of 0.9745. Moreover, A recommendation team has been assigned to assess the sample of the patient as well as suggest the patient according to classified results by the CAD.

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“…Using IoMT technology, patients and diagnostic laboratories may access data online from anywhere and at any time [ 6 ]. IoMT-assisted techniques bring significant advances to a range of medical fields that need rigorous study and supervision, including early diagnoses, such as diabetes [ 5 ], heart disease [ 7 ], infectious diseases [ 8 ], as well cancer diseases [ 9 ]. These diseases are detected early on and tracked utilizing IoT-based medical technologies.…”

Section: Introductionmentioning

confidence: 99%

An IoMT-Based Melanoma Lesion Segmentation Using Conditional Generative Adversarial Networks

Ali

Naz

Zaffar

et al. 2023

Sensors

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Currently, Internet of medical things-based technologies provide a foundation for remote data collection and medical assistance for various diseases. Along with developments in computer vision, the application of Artificial Intelligence and Deep Learning in IOMT devices aids in the design of effective CAD systems for various diseases such as melanoma cancer even in the absence of experts. However, accurate segmentation of melanoma skin lesions from images by CAD systems is necessary to carry out an effective diagnosis. Nevertheless, the visual similarity between normal and melanoma lesions is very high, which leads to less accuracy of various traditional, parametric, and deep learning-based methods. Hence, as a solution to the challenge of accurate segmentation, we propose an advanced generative deep learning model called the Conditional Generative Adversarial Network (cGAN) for lesion segmentation. In the suggested technique, the generation of segmented images is conditional on dermoscopic images of skin lesions to generate accurate segmentation. We assessed the proposed model using three distinct datasets including DermQuest, DermIS, and ISCI2016, and attained optimal segmentation results of 99%, 97%, and 95% performance accuracy, respectively.

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Internet of Medical Things (IoMT) Enabled Skin Lesion Detection and Classification Using Optimal Segmentation and Restricted Boltzmann Machines

Cited by 11 publications

References 13 publications

Swarm Intelligence in Internet of Medical Things: A Review

Swarm Intelligence in Internet of Medical Things: A Review

A Secure Framework toward IoMT‐Assisted Data Collection, Modeling, and Classification for Intelligent Dermatology Healthcare Services

An IoMT-Based Melanoma Lesion Segmentation Using Conditional Generative Adversarial Networks

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