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The quality of computer vision systems to detect abnormalities in various medical imaging processes, such as dual-energy X-ray absorptiometry, magnetic resonance imaging (MRI), ultrasonography, and computed tomography, has significantly improved as a result of recent developments in the field of deep learning. There is discussion of current techniques and algorithms for identifying, categorizing, and detecting DFU. On the small datasets, a variety of techniques based on traditional machine learning and image processing are utilized to find the DFU. These literary works have kept their datasets and algorithms private. Therefore, the need for end-to-end automated systems that can identify DFU of all grades and stages is critical. The study's goals were to create new CNN-based automatic segmentation techniques to separate surrounding skin from DFU on full foot images because surrounding skin serves as a critical visual cue for evaluating the progression of DFU as well as to create reliable and portable deep learning techniques for localizing DFU that can be applied to mobile devices for remote monitoring. The second goal was to examine the various diabetic foot diseases in accordance with well-known medical categorization schemes. According to a computer vision viewpoint, the authors looked at the various DFU circumstances including site, infection, neuropathy, bacterial infection, area, and depth. Machine learning techniques have been utilized in this study to identify key DFU situations as ischemia and bacterial infection.

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Routing in Wireless Sensor networks greatly effects lifetime of the network as a major portion of the available energy is utilized for data transmission among nodes, and these nodes are very constrained in terms of their battery power, and it is not feasible to replace the battery over time. So specialized protocols are needed for data transmission in WSNs to develop such routing strategies that minimize the power consumption to enhance the overall lifetime of WSNs, but at the same time maintain the throughput of the network. Chain-based routing protocols try to achieve this by forming a chain of sensor nodes wherein each node pass on the data to its closest neighbor while the cluster based protocol divide the whole network into small clusters and then each node transmit the data to its respective cluster head. The protocol proposed in this paper utilizes both clustering and chaining strategies, and also the coordinator nodes are used to get an optimal path for data transmission that enhances the overall lifetime of the WSN. In the beginning, clusters of all the nodes are formed and then within each cluster, a chain is formed. Along with it, coordinator nodes are used for inter-cluster communication. These nodes receive the data from the lower level cluster which is transmitted to the respective chain head from where it is again transmitted to the upper cluster. With the help of simulation of the proposed protocol, it is observed that the routing strategy of the protocol outperforms Chain Based Cluster Cooperative Protocol (CBCCP) [9] and Power-Efficient Gathering in Sensor Information Systems (PEGASIS) [4] protocols in terms of the overall lifetime of the network.