Blockchain-Assisted Homomorphic Encryption Approach for Skin Lesion Diagnosis using Optimal Deep Learning Model

Blockchain (BC) and Machine learning (ML) technologies have been investigated for potential applications in medicine with reasonable success to date. On the other hand, as accurate early diagnosis of skin lesion classification is essential gradually increase survival rate patient, Deep-Learning (DL) ML were introduced supporting dermatologists overcome these challenges. This study designed a Assisted Homomorphic Encryption Approach Skin Lesion Diagnosis using an Optimal Deep Learning (BHESKD-ODL) model. The presented BHESKD-ODL model achieves security proper images BC store medical patients restrict access third-party users or intruders. In addition, method secures mayfly optimization (MFO) algorithm (HE) technique. For diagnosis, proposed uses pre-processing Adam optimizer Fully Convolutional Network (FCN) based segmentation process. Furthermore, radiomics feature extraction Bidirectional Recurrent Neural (BiRNN) was employed classification. Finally, Red Deer Optimization (RDO) used optimal hyperparameter selection BiRNN approach. experimental results system on benchmark dataset proved its promising performance terms different measures.