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In traditional optimizers in CNNs, the momentum is usually weighted by a constant. However, tuning hyperparameters for momentum can be computationally complex. In this paper, we propose a novel adaptive momentum for fast and stable convergence.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Method<\/jats:title>\n                <jats:p>Applying adaptive momentum rate proposes increasing or decreasing based on every epoch's error changes, and it eliminates the need for momentum hyperparameter optimization. We tested the proposed method with 3 different datasets: REMBRANDT Brain Cancer, NIH Chest X-ray, COVID-19 CT scan. We compared the performance of a novel adaptive momentum optimizer with Stochastic gradient descent (SGD) and other adaptive optimizers such as Adam and RMSprop.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>Proposed method improves SGD performance by reducing classification error from 6.12 to 5.44%, and it achieved the lowest error and highest accuracy compared with other optimizers. To strengthen the outcomes of this study, we investigated the performance comparison for the state-of-the-art CNN architectures with adaptive momentum. The results shows that the proposed method achieved the highest with 95% compared to state-of-the-art CNN architectures while using the same dataset. The proposed method improves convergence performance by reducing classification error and achieves high accuracy compared with other optimizers.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s12880-022-00755-z","type":"journal-article","created":{"date-parts":[[2022,3,1]],"date-time":"2022-03-01T15:02:38Z","timestamp":1646146958000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":38,"title":["A novel adaptive momentum method for medical image classification using convolutional neural network"],"prefix":"10.1186","volume":"22","author":[{"given":"Utku Can","family":"Ayta\u00e7","sequence":"first","affiliation":[]},{"given":"Ali","family":"G\u00fcne\u015f","sequence":"additional","affiliation":[]},{"given":"Naim","family":"Ajlouni","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,3,1]]},"reference":[{"key":"755_CR1","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1186\/s40537-021-00444-8","volume":"8","author":"L Alzubaidi","year":"2021","unstructured":"Alzubaidi L, et al. 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