Internet of Things-Assisted Smart Skin Cancer Detection Using Metaheuristics with Deep Learning Model

SIMPLE SUMMARY: The Internet of Things (IoT) uses connected devices and sensors, like high-resolution cameras and specific sensors in wearable devices, for the collection of skin images with abnormalities. Skin cancer detection is difficult because of differences in lesion size, shape, and lighting conditions. To address this, an innovative approach called “ODL-SCDC”, combining deep learning with IoT technology, is developed. The proposed model uses advanced techniques like hyperparameter selection and feature extraction to improve skin cancer classification. The results show that ODL-SCDC outperforms other methods in accurately identifying skin lesions, which could have a significant impact on early cancer detection in the medical field. ABSTRACT: Internet of Things (IoT)-assisted skin cancer recognition integrates several connected devices and sensors for supporting the primary analysis and monitoring of skin conditions. A preliminary analysis of skin cancer images is extremely difficult because of factors such as distinct sizes and shapes of lesions, differences in color illumination, and light reflections on the skin surface. In recent times, IoT-based skin cancer recognition utilizing deep learning (DL) has been used for enhancing the early analysis and monitoring of skin cancer. This article presents an optimal deep learning-based skin cancer detection and classification (ODL-SCDC) methodology in the IoT environment. The goal of the ODL-SCDC technique is to exploit metaheuristic-based hyperparameter selection approaches with a DL model for skin cancer classification. The ODL-SCDC methodology involves an arithmetic optimization algorithm (AOA) with the EfficientNet model for feature extraction. For skin cancer detection, a stacked denoising autoencoder (SDAE) classification model has been used. Lastly, the dragonfly algorithm (DFA) is utilized for the optimal hyperparameter selection of the SDAE algorithm. The simulation validation of the ODL-SCDC methodology has been tested on a benchmark ISIC skin lesion database. The extensive outcomes reported a better solution of the ODL-SCDC methodology compared with other models, with a maximum sensitivity of 97.74%, specificity of 99.71%, and accuracy of 99.55%. The proposed model can assist medical professionals, specifically dermatologists and potentially other healthcare practitioners, in the skin cancer diagnosis process.