The healthcare area is entirely different from other industries. It is of the highly significant area and people supposed to gain the utmost care and facilities irrespective of the cost. Reliable image detection and classification is considered a significant capability in medical image investigation problems. The key challenge is that the whole image has to be searched for a particular event and then classified accordingly but it is necessary to ensure that any important piece of information or instance shouldn't be skipped. With regards to image analysis by radiologists, it is quite restricted because of its partiality, the intricacy of the images, wide variations that happen amongst various analysts and weariness. However, the introduction of deep learning is a promising way to improve this situation by sorting out the issue according to human leaning mechanism consequently it brings high-tech changes in medical image classification problems. In this context, a new ensemble deep learning topology is being proposed in the direction of a more precise classification of musculoskeletal ailments. In this regard, a comparison has been accomplished based on different learning rates, drop-out rates, and optimizers. This comparative research proved to be a baseline to gauge the up-to-the-mark performance of the proposed ensemble deep learning architecture.

During the recent decade, wireless body area network (WBAN) was developed and prioritized. This gives reliability, energy efficiency, and guaranteed results. Moreover, internet of medical things (IoMT) also enhances the significance of WBAN networks. To achieve high throughput, performance, and efficiency, WBAN deserves a new protocol definition as compared to general wireless sensor network along with a more enhanced framework. The standard 802.15.6 with PHY and MAC layers follow the standardization of WBAN. The wireless nature of the network and various varieties of sensors in the presence of IoMT made it possible to develop new, effective, innovative, and demand-driven solutions for health improvement and quality of service. In the recent literature, the researchers have proposed an IoMT-based secure framework for WBAN. In this chapter, an in-depth and comprehensive depiction of the security issues of IoMT-based framework of the wireless network is highlighted that incorporates security measures in different levels of the WBAN network.

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