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In this study, a novel deep-learning algorithm named (CM-Net) was developed to classify biological data obtained as images from Confocal Microscopy. The images were collected for two types of bacterial species: (\n                    <jats:italic>Escherichia coli<\/jats:italic>\n                    and\n                    <jats:italic>Staphylococcus aureus<\/jats:italic>\n                    ), where the number of images was 300 for each class. To enhance the dataset, we divided each image (using the augmentation method) into a small number of images with 224\u2009\u00d7\u2009224 dimensions, resulting in a total of 7066 images for both classes. These augmented images were fed to CM-Net to ensure accurate results and avoid bias in the developed algorithms. The algorithm was trained and tested 30 times with a 5-K cross-validation for each time. The algorithm\u2019s performance was evaluated using seven metrics (accuracy, sensitivity, specificity, precision, NVA, F1-score, and MCC), where the respective results were 96.08%, 95.98%, 96.19%, 96.78%, 95.26%, 96.38%, and 92.11%, indicating the model\u2019s high accuracy and reliability. CM-Net drastically reduces bacterial identification time by automating large-scale data analysis, processing results in 8.9\u00a0min. The automation provided by CM-Net simplifies workflows, enabling non-expert workers to perform microbial identification without extensive training. The significant outcomes of applying CM-Net for bacterial identification revolve around its transformative impact on data analysis\u2019s speed, efficiency, and accuracy, making advanced analysis accessible to non-experts while minimizing human error.\n                  <\/jats:p>","DOI":"10.1038\/s41598-026-38861-5","type":"journal-article","created":{"date-parts":[[2026,2,9]],"date-time":"2026-02-09T23:17:08Z","timestamp":1770679028000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Novel convolutional neural network for bacterial identification of confocal microscopic datasets"],"prefix":"10.1038","volume":"16","author":[{"given":"Ahmed","family":"Al-Jumaili","sequence":"first","affiliation":[]},{"given":"Saif","family":"Al-Jumaili","sequence":"additional","affiliation":[]},{"given":"Salam","family":"Alyassri","sequence":"additional","affiliation":[]},{"given":"Adil Deniz","family":"Duru","sequence":"additional","affiliation":[]},{"given":"Osman Nuri","family":"U\u00e7an","sequence":"additional","affiliation":[]},{"given":"Mohan V.","family":"Jacob","sequence":"additional","affiliation":[]},{"given":"Frederico","family":"Branco","sequence":"additional","affiliation":[]},{"given":"Paulo Jorge","family":"Coelho","sequence":"additional","affiliation":[]},{"given":"Ivan Miguel","family":"Pires","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2026,2,10]]},"reference":[{"issue":"6","key":"38861_CR1","doi-asserted-by":"publisher","first-page":"420","DOI":"10.1007\/s42979-021-00815-1","volume":"2","author":"IH Sarker","year":"2021","unstructured":"Sarker, I. 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