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

Abstract

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.

Segmentation and classification of skin lesions using hybrid deep learning method in the Internet of Medical Things

“…In addition, IoMT paves the door for telemedicine, which allows patients to consult with dermatologists remotely, increases the number of readily available specialists, and guarantees that patients receive treatment quickly 10 . With IoMT and deep learning, skin cancer detection could be greatly enhanced, allowing for earlier diagnosis and treatment for many more people 11,12 …”

“…10 With IoMT and deep learning, skin cancer detection could be greatly enhanced, allowing for earlier diagnosis and treatment for many more people. 11,12 In dermoscopy, a tiny region of skin is magnified and illuminated with the help of handheld equipment called a dermatoscopy. This procedure makes better observation and identification of the pigment network, vascular patterns, and other structures associated with skin cancer possible.…”

A Deep Auto-Optimized Collaborative Learning (DACL) model for disease prognosis using AI-IoMT systems

“…In fact, the IoMT blends traditional IoT's dynamic, flexible, and scalability features with the dependability and safety of conventional medical devices 15 – 17 . By managing multiple gadgets deployed for numerous patients and being sufficiently generic to handle a variety of conditions calling for numerous monitoring and controlling demands, it has the potential to overcome the problem of aging and chronic diseases 18 . In addition, IoMT offers a solution for other problems including patient transportation (i.e., constant monitoring of patients in their daily activities as contrasted with telemedicine systems, which are primarily concerned with home-care).…”

Two-step hierarchical binary classification of cancerous skin lesions using transfer learning and the random forest algorithm

“…Additionally, the high resolution and heterogeneity of skin lesions, along with factors like hair causing clutter, further complicate dermoscopy diagnosis. Thus, there is a need for advanced computer-aided diagnosis (CAD) techniques, potentially coupled with Internet of Medical Things (IoMT) devices, to automate screening and early detection of skin cancer [18,19]. Big data, computer vision, and artificial intelligence (AI) technologies, including machine and deep learning (DL) techniques, have been employed in various medical contexts, including disease diagnosis and treatment optimization [20].…”