MIoT Based Skin Cancer Detection Using Bregman Recurrent Deep Learning

Mobile clouds are the most common medium for aggregating, storing, and analyzing data from the medical Internet of Things (MIoT). It is employed to monitor a patient’s essential health signs for earlier disease diagnosis and prediction. Among the various disease, skin cancer was the wide variety of cancer, as well as enhances the endurance rate. In recent years, many skin cancer classification systems using machine and deep learning models have been developed for classifying skin tumors, including malignant melanoma (MM) and other skin cancers. However, accurate cancer detection was not performed with minimum time consumption. In order to address these existing problems, a novel Multidimensional Bregman Divergencive Feature Scaling Based Cophenetic Piecewise Regression Recurrent Deep Learning Classification (MBDFS-CPRRDLC) technique is introduced for detecting cancer at an earlier stage. The MBDFS-CPRRDLC performs skin cancer detection using different layers such as input, hidden, and output for feature selection and classification. The patient information is composed of IoT. The patient information was stored in mobile clouds server for performing predictive analytics. The collected data are sent to the recurrent deep learning classifier. In the first hidden layer, the feature selection process is carried out using the Multidimensional Bregman Divergencive Feature Scaling technique to find the significant features for disease identification resulting in decreases time consumption. Followed by, the disease classification is carried out in the second hidden layer using cophenetic correlative piecewise regression for analyzing the testing and training data. This process is repeatedly performed until the error gets minimized. In this way, disease classification is accurately performed with higher accuracy. Experimental evaluation is carried out for factors namely Accuracy, precision, recall, F-measure, as well as cancer detection time, by the amount of patient data. The observed result confirms that the proposed MBDFS-CPRRDLC technique increases accuracy as well as lesser cancer detection time compared to the conventional approaches.