## 6 Conclusions  
In this paper, we proposed a blind IQA method based on a deep CNN to solve the difficulty of finding the clearest image in a microscopic image group captured in the autofocus process, namely GMANet. We introduced the gradient information into a low-level convolution block as spatial attention to make the high-level features pay more attention to sharp regions. Experimental results show that the proposed network has good consistency with human visual properties. As gradient images are not features to be learned, the deep model trained on the feces dataset is universal, and can be applied to leucorrhea and blood microscopic image quality assessment without additional transfer learning. Our study has value for addressing the autofocus task for microscopic images with complex composition. The following are available online at [[https://www.mdpi.com/article/10.3390/app112110293/s1](https://www.mdpi.com/article/10.3390/app112110293/s1)]([https://www.mdpi.com/article/10.3390/app112110293/s1](https://www.mdpi.com/article/10.3390/app112110293/s1)), Experiment 1: Using traditional IQA methods to find clearest fecal microscopic image, Experiment 2: Using resnet50 as GMANet backbone, Experiment 3: The performance of GMANet on assessing the clarity of objects. Project administration, L.L. ; investigation, G.N. ; resources, G.N. ; data curation, X.W. ; methodology, X.W. ; writing--original draft preparation, X.W. ; writing--review and editing, X.D. ; funding acquisition, J.Z. and J.L. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Natural Science Foundation of China (No. 61905036), the Fundamental Research Funds for the Central Universities (University of Electronic Science and Technology of China) (No. ZYGX2019J053), and the China Postdoctoral Science Foundation (2019M663465). Figure 12: The influence of epithelial cells with large size on prediction results. (**a**,**b**) are the annotated and predicted clearest image in one image group, respectively. **Institutional Review Board Statement:** The study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Institutional Review Board of the University of Electronic Science and Technology of China (protocol code: 106142021030903). **Informed Consent Statement:** Written informed consent was obtained from the patients to publish this paper. All samples were anonymized. **Data Availability Statement:** The algorithm codes will be released online at www.github.com/wvx29 2/GMANet, accessed on 1 November 2021. **Acknowledgments:** We would like to express our thanks to [PERSON] and the staff at the MOEMIL laboratory, who collected and counted the samples used in this study. **Conflicts of Interest:** The authors declare no conflict of interest. * (21) [PERSON]; [PERSON]; [PERSON]; IEEE. RankIQA: Learning from Rankings for No-reference Image Quality Assessment. In Proceedings of the 16 th IEEE International Conference on Computer Vision (ICCV), Venice, Italy, 22-29 October 2017; pp. 1040-1049. * (22) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Waterloo Exploration Database: New Challenges for Image Quality Assessment Models. _IEEE Trans. Image Process._**2017**, _26_, 1004-1016. [CrossRef]
* (23) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Learning Deep Features for Scene Recognition using Places Database. In Proceedings of the 28 th Conference on Neural Information Processing Systems (NIPS), Montreal, QC, Canada, 8-13 December 2014. * (24) [PERSON]; [PERSON]; [PERSON]; [PERSON] Entropy Based Data Expansion Method for Blind Image Quality Assessment. _Entropy_**2020**, _22_, 60. [CrossRef] [PubMed]
* (25) [PERSON]; [PERSON]; [PERSON] MANet: A two-stage deep learning method for classification of COVID-19 from Chest X-ray images. _Neurocomputing_**2021**, _443_, 96-105. [CrossRef]
* (26) [PERSON]; [PERSON]; [PERSON] S-3: A Spectral and Spatial Measure of Local Perceived Sharpness in Natural Images. _IEEE Trans.