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Some severe or inflammatory acne leads to scars, which may have major impacts on patients\u2019 quality of life or even job prospects. Grading acne plays an important role in diagnosis, and the diagnosis is made by counting the number of acne. It is a labor\u2010intensive job and it is easy for dermatologists to make mistakes, so it is very important to develop automatic diagnosis methods. Ensemble learning may improve the prediction results of the base models, but its time complexity is relatively high. The ensemble pruning strategy may solve this computational challenge by removing the redundant base models.<\/jats:p><\/jats:sec><jats:sec><jats:title>Materials and methods<\/jats:title><jats:p>This study proposed a novel ensemble pruning framework of deep learning models to accurately detect and grade acne using images. First, we train multi\u2010base models and prune the redundancy models according to the performance and diversity of the models. Then, we construct the new features of the training data by the base models we select in the previous step. Next, we remove the redundancy models further by a feature selection algorithm. Finally, we integrate all the base models by classifiers. The ensemble pruning algorithm was proposed to prune the deep learning base models.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The experimental data showed that the ensemble pruned framework achieved a prediction accuracy of 85.82% on the acne dataset, better than the existing studies. To verify our method's effectiveness, we test our method in a skin cancer dataset and greatly outperform the state\u2010of\u2010the\u2010art methods.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>The method we proposed is used to grade acne. Our method's performance outperforms state\u2010of\u2010the\u2010art methods on two datasets, and it can also remove redundancy models to reduce computational complexity.<\/jats:p><\/jats:sec>","DOI":"10.1111\/srt.13166","type":"journal-article","created":{"date-parts":[[2022,5,31]],"date-time":"2022-05-31T17:33:49Z","timestamp":1654018429000},"page":"677-688","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["AcneGrader: An ensemble pruning of the deep learning base models to grade acne"],"prefix":"10.1111","volume":"28","author":[{"given":"Shuai","family":"Liu","sequence":"first","affiliation":[{"name":"College of Computer Science and Technology Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University  Changchun Jilin P.R. 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