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Few-view computed tomography (CT) is able to provide the internal information of an object for diagnosis, while reducing radiation dose and decreasing side effects. In spite of these benefits, few-view CT images with conventional reconstruction algorithms are suffer from structural distortion and artifacts due to insufficient projections. In this study, a deep learning-based network was proposed for reconstructing few-view CT images and improving image quality. The proposed network consisted of fully connected, convolution and deconvolution layers, and the network training was implemented to directly transform few-view projections into reconstructed images without expert reconstruction knowledge. The quality of the output images obtained from the trained network was compared with those reconstructed by using the filtered back-projection (FBP) and simultaneous algebraic reconstruction technique (SART) algorithms. The results showed that the proposed network is able to improve the quantitative accuracy and noise property of reconstructed images, and the proposed network is more suitable for few-view CT reconstruction than the other algorithms. In conclusion, the proposed network can be potentially used to improve few-view CT image quality and considered as an alternative for few-view CT reconstruction. The source code is available on a GitHub repository at https://github.com/ YIMDOBIN/CT_reconstruction_via_DL. • A deep learning-based network was proposed for reconstructing few-view CT images. • The proposed network suppressed artifacts in CT images. • The quantitative accuracy of CT images was improved by using the proposed network. • The proposed network was able to reduce the noise components of few-view CT images. • The proposed network performance was noticeable for the small number of projections. [ABSTRACT FROM AUTHOR]