No abstract

Medical image information can be exchanged remotely through cloud-based medical imaging services. Digital Imaging and Communication in Medicine (DICOM) is considered to be the most commonly used medical image format among hospitals. The objective of this article is to enhance the secure transfer and storage of medical images on the cloud by using hybrid encryption algorithms, which are a combination of symmetric encryption algorithms and asymmetric encryption algorithms that make the encryption process faster and more secure. To this end, three different algorithms are chosen to build the framework. These algorithms are simple and suitable for hardware or software implementation because they require low memory and low computational power yet provide high security. Also, security was increased by using a digital signature technique. The results of the analyses showed that for a DICOM file with size 12.5 Mb, 2.957 minutes was required to complete the process. This was totaled from the encryption process took 1.898 minutes, and the decryption process took 1.059 minutes.

The last two years are considered the most crucial and critical period of the COVID-19 pandemic affecting most life aspects worldwide. This virus spreads quickly within a short period, increasing the fatality rate associated with the virus. From a clinical perspective, several diagnosis methods are carried out for early detection to avoid virus propagation. However, the capabilities of these methods are limited and have various associated challenges. Consequently, many studies have been performed for COVID-19 automated detection without involving manual intervention and allowing an accurate and fast decision. As is the case with other diseases and medical issues, Artificial Intelligence (AI) provides the medical community with potential technical solutions that help doctors and radiologists diagnose based on chest images. In this paper, a comprehensive review of the mentioned AI-based detection solution proposals is conducted. More than 200 papers are reviewed and analyzed, and 145 articles have been extensively examined to specify the proposed AI mechanisms with chest medical images. A comprehensive examination of the associated advantages and shortcomings is illustrated and summarized. Several findings are concluded as a result of a deep analysis of all the previous works using machine learning for COVID-19 detection, segmentation, and classification.

Brain tumour segmentation can improve diagnostics efficiency, rise the prediction rate and treatment planning. This will help the doctors and experts in their work. Where many types of brain tumour may be classified easily, the gliomas tumour is challenging to be segmented because of the diffusion between the tumour and the surrounding edema. Another important challenge with this type of brain tumour is that the tumour may grow anywhere in the brain with different shape and size. Brain cancer presents one of the most famous diseases over the world, which encourage the researchers to find a high-throughput system for tumour detection and classification. Several approaches have been proposed to design automatic detection and classification systems. This paper presents an integrated framework to segment the gliomas brain tumour automatically using pixel clustering for the MRI images foreground and background and classify its type based on deep learning mechanism, which is the convolutional neural network. In this work, a novel segmentation and classification system is proposed to detect the tumour cells and classify the brain image if it is healthy or not. After collecting data for healthy and non-healthy brain images, satisfactory results are found and registered using computer vision approaches. This approach can be used as a part of a bigger diagnosis system for breast tumour detection and manipulation.

The Internet of Things (IoT) is a technology that allows machines to communicate with each other without the need for human interaction. Usually, IoT devices are connected via a network. A wide range of network technologies are required to make the IoT concept operate successfully; as a result, protocols at various network layers are used. One of the most extensively used network layer routing protocols is the Routing Protocol for Low Power and Lossy Networks (RPL). One of the primary components of RPL is the trickle timer method. The trickle algorithm directly impacts the time it takes for control messages to arrive. It has a listen-only period, which causes load imbalance and delays for nodes in the trickle algorithm. By making the trickle timer method run dynamically based on hop count, this research proposed a novel way of dealing with the difficulties of the traditional algorithm, which is called the Elastic Hop Count Trickle Timer Algorithm. Simulation experiments have been implemented using the Contiki Cooja 3.0 simulator to study the performance of RPL employing the dynamic trickle timer approach. Simulation results proved that the proposed algorithm outperforms the results of the traditional trickle algorithm, dynamic algorithm, and e-trickle algorithm in terms of consumed power, convergence time, and packet delivery ratio.