Computed tomography (CT) is used in a wide range of medical imaging diagnoses. However, the reconstruction of CT images from raw projection data is inherently complex and is subject to artifacts and noise, which compromises image quality and accuracy. In order to address these challenges, deep learning developments have the potential to improve the reconstruction of computed tomography images. In this regard, our research aim is to determine the techniques that are used for 3D deep learning in CT reconstruction and to identify the training and validation datasets that are accessible. This research was performed on five databases. After a careful assessment of each record based on the objective and scope of the study, we selected 60 research articles for this review. This systematic literature review revealed that convolutional neural networks (CNNs), 3D convolutional neural networks (3D CNNs), and deep learning reconstruction (DLR) were the most suitable deep learning algorithms for CT reconstruction. Additionally, two major datasets appropriate for training and developing deep learning systems were identified: 2016 NIH-AAPM-Mayo and MSCT. These datasets are important resources for the creation and assessment of CT reconstruction models. According to the results, 3D deep learning may increase the effectiveness of CT image reconstruction, boost image quality, and lower radiation exposure. By using these deep learning approaches, CT image reconstruction may be made more precise and effective, improving patient outcomes, diagnostic accuracy, and healthcare system productivity.

In disaster response scenarios, the formation of multi-UAV networks is a critical task. The dynamic nature of UAVs makes it difficult to employ traditional approaches. This research study presents an improved task allocation mechanism using UAV localization in disaster conditions. Due to the random movement of UAVs localization is quite a complex problem. Therefore, a novel approach is introduced which is based on received signal strength indicator controlled genetic neural fusion. Genetic neural fusion is the combination of a genetic algorithm and an artificial neural network that adjusts weights to improve localization. During simulation 3D environment is properly built for UAVs. The proposed algorithm GenNF is compared with other techniques like CNN-RNN, CNN-LSTM, CNN-GRU, ANN-LSTM, Particle Filter, and Kalman Filter. GenNF has shown better performance regarding the mean square error, root mean square error, and huber loss. RSSI enabled GenNF to accurately calculate signal strength between UAVs and base station. Therefore, if the signal strength is strong then task will be accordingly allocated to UAVs.