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The people around the globe are suffering from different types of brain tumors. So, early prediction of brain tumors can save human lives. This work focused on implementation of secured brain tumor classification network (SBTC-Net) using transfer learning methods. Initially, security is achieved by performing the medical image watermarking (MIW) operation using translation invariant wavelet transform (TIWT). Here, the watermarking process covers the source MRI brain tumor image of patient with unknown medical image (cover image). Then, this watermarked image is transmitted over the Internet of Medical Things (IoMT) environment. Here, the attackers are unable to visualize the source image. So, the source brain tumor image is transmitted over a secured environment. At receiver of IoMT, the segmentation operation is performed using transfer learning-based Recurrent U-Net (RU-Net) model, which localizes exact area of tumor. In addition, multilevel features are extracted using black widow optimization-genetic algorithm (BWO-GA), which selects the best features using natural inspired properties. Further, transfer learning based AlexNet is used to train with the optimal features, which classifies the benign and malignant tumors. Finally, the simulation results show that the proposed SBTC-Net resulted in superior watermarking, segmentation, and classification performance in terms subjective visualization and objective metrics as compared to state of art approaches. The proposed SBTC-Net achieved 99.97% of segmentation accuracy, 99.98% of classification accuracy on BraTS-2020 dataset.

SBTC-Net: Secured Brain Tumor Segmentation and Classification Using Black Widow with Genetic Optimization in IoMT