This paper summarizes the literature on computer-aided detection (CAD) systems used to identify and diagnose lung nodules in images obtained with computed tomography (CT) scanners. The importance of developing such systems lies in the fact that the process of manually detecting lung nodules is painstaking and sequential work for radiologists, as it takes a long time. Moreover, the pulmonary nodules have multiple appearances and shapes, and the large number of slices generated by the scanner creates great difficulty in accurately locating the lung nodules. The handcraft nodules detection process can be caused by messing some nodules spicily when these nodules' diameter be less than 10 mm. So, the CAD system is an essential assistant to the radiologist in this case of nodule detection, and it contributed to reducing time consumption in nodules detection; moreover, it applied more accuracy in this field. The objective of this paper is to follow up on current and previous work on lung cancer detection and lung nodule diagnosis. This literature dealt with a group of specialized systems in this field quickly and showed the methods used in them. It dealt with an emphasis on a system based on deep learning involving neural convolution networks.

<span>The present work was an attempt to address a challenge of energy utilization or consumption in WBSN, it used a proposed routing algorithm based on ant optimization technique to distribute energy utilization efficiently on nodes. Thus reducing consumed energy and extending the life cycle of nodes, as well as avoiding damages might be occurred in tissues of patient`s body. At the beginning the proposed protocol was compared with conventional routing protocol LEACH to prove it`s efficiency in the extending of the life cycle of node, then it was used with experimental network was employed to examine energy utilizations. The obtained results were compared with others attained by conventional and developed routing protocols, there was considerable minimizing in the energy consumption that proved efficiency of proposed algorithm.</span>

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