Hyperparameter Optimization of Deep Neural Network in Multimodality Fused Medical Image Classification for Medical and Industrial IoT

Industrial Internet of Things (IIoT) refers to the extension and utilization of the Internet of Things (IoT) in industrial sectors and applications. Medical image fusion and classification has been utilized to get valuable significant multimodality medical image data. Recently, deep learning methods offer an effective way to design an end-to-end model that can determine the final classification labels. This chapter introduces a Multimodality Image Fusion Classification (MMIFC) by the incorporation of image fusion, feature extraction, and classification techniques. Initially, the input medical images were fused by the use of optimal shearlet transform where the coefficients of shearlets are optimized by the use of Enhanced Monarch Butterfly Optimization (EMBO) algorithm. In the next stage, the fused images were classified based on the feature extractor and deep learning model to check the test image is malignant and benign. To further optimize the performance of the deep learning model, hyperparameter tuning process takes place by the use of Bayesian optimization model to optimize the weights of structure. Finally, this ODNN model classifies the MMFI as class 1 or 0. A detailed simulation result takes place to ensure the effective performance of the proposed method. From the experimental results, the proposed strategy accomplishes better fusion rate and classification results compared with other supervised and non-supervised learning techniques.