Diagnosis and treatment of hepatocellular carcinoma or metastases rely heavily on accurate segmentation and classification of liver tumours. However, due to the liver tumor’s hazy borders and wide range of possible shapes, sizes, and positions, accurate and automatic tumour segmentation and classification remains a difficult challenge. With the advancement of computing, new models in artificial intelligence have evolved. Following its success in Natural language processing (NLP), the transformer paradigm has been adopted by the computer vision (CV) community of the NLP. While there are already accepted approaches to classifying the liver, especially in clinical settings, there is room for advancement in terms of their precision. This paper makes an effort to apply a novel model for segmenting and classifying liver tumours built on deep learning. In order to accomplish this, the created model follows a three-stage procedure consisting of (a) pre-processing, (b) liver segmentation, and (c) classification. In the first phase, the collected Computed Tomography (CT) images undergo three stages of pre-processing, including contrast improvement via histogram equalization and noise reduction via the median filter. Next, an enhanced mask region-based convolutional neural networks (Mask R-CNN) model is used to separate the liver from the CT abdominal image. To prevent overfitting, the segmented picture is fed onto an Enhanced Swin Transformer Network with Adversarial Propagation (APESTNet). The experimental results prove the superior performance of the proposed perfect on a wide variety of CT images, as well as its efficiency and low sensitivity to noise.
The expansion of the Internet of Things is expected to lead to the emergence of the Internet of Medical Things (IoMT), which will revolutionize the health-care industry (IoT). The Internet of Things (IoT) revolution is outpacing current human services thanks to its bright mechanical, economical, and social future. Security is essential because most patient information is housed on a cloud platform in the hospital. The security of medical images in the Internet of Things was investigated in this research using a new cryptographic model and optimization approaches. For the effective storage and safe transfer of patient data along with medical images, a separate framework is required. The key management and optimization will be chosen utilizing the Rivest–Shamir–Adleman-based Arnold map (RSA-AM), hostile orchestration (HO), and obstruction bloom breeding optimization (OBBO) to increase the encryption and decryption processes’ level of security. The effectiveness of the suggested strategy is measured using peak signal-to-noise ratio (PSNR), entropy, mean square error (MSE), bit error rate (BER), structural similarity index (SSI), and correlation coefficient (CC). The investigation shows that the recommended approach provides greater security than other current systems.
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