{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,20]],"date-time":"2026-01-20T11:46:04Z","timestamp":1768909564898,"version":"3.49.0"},"reference-count":41,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2025,2,9]],"date-time":"2025-02-09T00:00:00Z","timestamp":1739059200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100012725","name":"Tecnologico Nacional de Mexico","doi-asserted-by":"publisher","award":["19182.24-P"],"award-info":[{"award-number":["19182.24-P"]}],"id":[{"id":"10.13039\/100012725","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["BDCC"],"abstract":"<jats:p>Integrating deep learning into microbiological and cell analysis from microscopic image samples has gained significant attention in recent years, driven by the rise of novel medical technologies and pressing global health challenges. Numerous methods for segmentation and classification in microscopic images have emerged in the literature. However, key challenges persist due to the limited development of specialized deep learning models to accurately detect and quantify microorganisms and cells from microscopic samples. In response to this gap, this paper introduces MBnet, an Extreme-Lightweight Neural Network for Microbiological and Cell Analysis. MBnet is a binary segmentation method based on a Fully Convolutional Network designed to detect and quantify microorganisms and cells, featuring a low computational cost architecture with only 575 parameters. Its innovative design includes a foreground module and an encoder\u2013decoder structure composed of traditional, depthwise, and separable convolution layers. These layers integrate color, orientation, and morphological features to generate an understanding of different contexts in microscopic sample images for binary segmentation. Experiments were conducted using datasets containing bacteria, yeast, and blood cells. The results suggest that MBnet outperforms other popular networks in the literature in counting, detecting, and segmenting cells and unicellular microorganisms. These findings underscore the potential of MBnet as a highly efficient solution for real-world applications in health monitoring and bioinformatics.<\/jats:p>","DOI":"10.3390\/bdcc9020036","type":"journal-article","created":{"date-parts":[[2025,2,10]],"date-time":"2025-02-10T06:43:07Z","timestamp":1739169787000},"page":"36","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Novel Extreme-Lightweight Fully Convolutional Network for Low Computational Cost in Microbiological and Cell Analysis: Detection, Quantification, and Segmentation"],"prefix":"10.3390","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4445-6555","authenticated-orcid":false,"given":"Juan A.","family":"Ramirez-Quintana","sequence":"first","affiliation":[{"name":"Graduate Studies and Research Division, Tecnologico Nacional de Mexico\/I.T. Chihuahua, Chihuahua 31200, Mexico"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-0161-3667","authenticated-orcid":false,"given":"Edgar A.","family":"Salazar-Gonzalez","sequence":"additional","affiliation":[{"name":"Graduate Studies and Research Division, Tecnologico Nacional de Mexico\/I.T. Chihuahua, Chihuahua 31200, Mexico"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5382-9424","authenticated-orcid":false,"given":"Mario I.","family":"Chacon-Murguia","sequence":"additional","affiliation":[{"name":"Graduate Studies and Research Division, Tecnologico Nacional de Mexico\/I.T. Chihuahua, Chihuahua 31200, Mexico"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5673-5992","authenticated-orcid":false,"given":"Carlos","family":"Arzate-Quintana","sequence":"additional","affiliation":[{"name":"Faculty of Medicine and Biomedical Sciences, Universidad Autonoma de Chihuahua Campus II, Chihuahua 31125, Mexico"}]}],"member":"1968","published-online":{"date-parts":[[2025,2,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1801","DOI":"10.1007\/s11831-021-09639-x","article-title":"Machine Learning and Deep Learning Based Computational Approaches in Automatic Microorganisms Image Recognition: Methodologies, Challenges, and Developments","volume":"29","author":"Rani","year":"2022","journal-title":"Arch. Comput. 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