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

Abstract

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.

Novel Computer-Aided Diagnosis System for the Early Detection of Alzheimer’s Disease

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A Framework for Interactive Medical Image Segmentation Using Optimized Swarm Intelligence with Convolutional Neural Networks

“…In one of our previous studies, we have developed an IoT-based data collection system for skin lesions where we classified various skin lesions using deep learning-based ensemble algorithms [ 21 ]. For skin lesion segmentation, Yuan et al [ 22 ] used the notion of Deep Fully Convolutional Neural Networks (DFCNN) with Jaccard distance.…”

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

“…These systems are used for virtual patient care or long-term illness monitoring as well as patient medication tracking. By linking patients to respective physicians as well as allowing healthcare data to be exchanged over a secure site that could be anywhere, the Internet of Things substantially lowered the number of needless hospitalizations [ 5 ]. Using IoMT technology, patients and diagnostic laboratories may access data online from anywhere and at any time [ 6 ].…”

“…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.…”