{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,7]],"date-time":"2026-05-07T04:25:00Z","timestamp":1778127900890,"version":"3.51.4"},"reference-count":56,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2021,4,24]],"date-time":"2021-04-24T00:00:00Z","timestamp":1619222400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Leukaemia is a dysfunction that affects the production of white blood cells in the bone marrow. Young cells are abnormally produced, replacing normal blood cells. Consequently, the person suffers problems in transporting oxygen and in fighting infections. This article proposes a convolutional neural network (CNN) named LeukNet that was inspired on convolutional blocks of VGG-16, but with smaller dense layers. To define the LeukNet parameters, we evaluated different CNNs models and fine-tuning methods using 18 image datasets, with different resolution, contrast, colour and texture characteristics. We applied data augmentation operations to expand the training dataset, and the 5-fold cross-validation led to an accuracy of 98.61%. To evaluate the CNNs generalisation ability, we applied a cross-dataset validation technique. The obtained accuracies using cross-dataset experiments on three datasets were 97.04, 82.46 and 70.24%, which overcome the accuracies obtained by current state-of-the-art methods. We conclude that using the most common and deepest CNNs may not be the best choice for applications where the images to be classified differ from those used in pre-training. Additionally, the adopted cross-dataset validation approach proved to be an excellent choice to evaluate the generalisation capability of a model, as it considers the model performance on unseen data, which is paramount for CAD systems.<\/jats:p>","DOI":"10.3390\/s21092989","type":"journal-article","created":{"date-parts":[[2021,4,25]],"date-time":"2021-04-25T02:12:57Z","timestamp":1619316777000},"page":"2989","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":39,"title":["Diagnosis of Leukaemia in Blood Slides Based on a Fine-Tuned and Highly Generalisable Deep Learning Model"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4690-3921","authenticated-orcid":false,"given":"Luis","family":"Vogado","sequence":"first","affiliation":[{"name":"Departamento de Computa\u00e7\u00e3o, Universidade Federal do Piau\u00ed, Teresina 64049-550, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8180-4032","authenticated-orcid":false,"given":"Rodrigo","family":"Veras","sequence":"additional","affiliation":[{"name":"Departamento de Computa\u00e7\u00e3o, Universidade Federal do Piau\u00ed, Teresina 64049-550, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3938-5089","authenticated-orcid":false,"given":"Kelson","family":"Aires","sequence":"additional","affiliation":[{"name":"Departamento de Computa\u00e7\u00e3o, Universidade Federal do Piau\u00ed, Teresina 64049-550, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2824-2645","authenticated-orcid":false,"given":"Fl\u00e1vio","family":"Ara\u00fajo","sequence":"additional","affiliation":[{"name":"Curso de Bacharelado em Sistemas de Informa\u00e7\u00e3o, Universidade Federal do Piau\u00ed, Picos 64607-670, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7163-7469","authenticated-orcid":false,"given":"Romuere","family":"Silva","sequence":"additional","affiliation":[{"name":"Curso de Bacharelado em Sistemas de Informa\u00e7\u00e3o, Universidade Federal do Piau\u00ed, Picos 64607-670, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2059-9463","authenticated-orcid":false,"given":"Moacir","family":"Ponti","sequence":"additional","affiliation":[{"name":"Instituto de Ci\u00eancias Matem\u00e1ticas de de Computa\u00e7\u00e3o, Universidade de S\u00e3o Paulo, S\u00e3o Carlos 13566-590, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7603-6526","authenticated-orcid":false,"given":"Jo\u00e3o Manuel R. 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Image Anal."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"6155","DOI":"10.1038\/s41598-018-24588-5","article-title":"Heterogeneity Analysis and Diagnosis of Complex Diseases Based on Deep Learning Method","volume":"8","author":"Li","year":"2018","journal-title":"Sci. Rep."},{"key":"ref_4","unstructured":"Dos Santos, F.P., and Ponti, M.A. (November, January 29). Robust feature spaces from pre-trained deep network layers for skin lesion classification. Proceedings of the 2018 31st SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI), Parana, Brazil."},{"key":"ref_5","unstructured":"Dos Santos, F.P., and Ponti, M.A. (2019, January 28\u201330). Alignment of Local and Global Features from Multiple Layers of Convolutional Neural Network for Image Classification. Proceedings of the 2019 32nd SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI), Rio de Janeiro, Brazil."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1016\/j.artmed.2014.09.002","article-title":"Leucocyte classification for leukaemia detection using image processing techniques","volume":"62","author":"Putzu","year":"2014","journal-title":"Artif. Intell. Med."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Vincent, I., Kwon, K.R., Lee, S.H., and Moon, K.S. (2015, January 28\u201330). Acute Lymphoid Leukemia Classification using Two-Step Neural Network Classifier. Proceedings of the Frontiers of Computer Vision (FCV), Mokpo, Korea.","DOI":"10.1109\/FCV.2015.7103739"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"635","DOI":"10.1016\/j.procs.2015.08.082","article-title":"Automated Leukaemia Detection Using Microscopic Images","volume":"58","author":"Patel","year":"2015","journal-title":"Procedia Comput. Sci."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Singhal, V., and Singh, P. (2016, January 1). Texture Features for the Detection of Acute Lymphoblastic Leukemia. Proceedings of the International Conference on ICT for Sustainable, Singapore.","DOI":"10.1007\/978-981-10-0135-2_52"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"661","DOI":"10.1109\/TPAMI.1984.4767591","article-title":"Fractal-Based Description of Natural Scenes","volume":"6","author":"Pentland","year":"1984","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/j.compmedimag.2019.01.003","article-title":"Deep learning for cell image segmentation and ranking","volume":"72","author":"Araujo","year":"2019","journal-title":"Comput. Med. Imaging Graph."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"6869","DOI":"10.1007\/s11042-018-6404-8","article-title":"ABCD rule and pre-trained CNNs for melanoma diagnosis","volume":"78","author":"Moura","year":"2018","journal-title":"Multimed. Tools Appl."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"101845","DOI":"10.1016\/j.artmed.2020.101845","article-title":"Breast cancer diagnosis from histopathological images using textural features and CBIR","volume":"105","author":"Carvalho","year":"2020","journal-title":"Artif. Intell. Med."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"54","DOI":"10.7763\/IJCTE.2018.V10.1198","article-title":"Leukemia Blood Cell Image Classification Using Convolutional Neural Network","volume":"10","author":"Thanh","year":"2018","journal-title":"Int. J. Comput. Theory Eng."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Ponti, M.A., Ribeiro, L.S.F., Nazare, T.S., Bui, T., and Collomosse, J. (2017, January 17\u201318). Everything you wanted to know about deep learning for computer vision but were afraid to ask. Proceedings of the 2017 30th SIBGRAPI Conference on Graphics, Patterns and Images Tutorials (SIBGRAPI-T), Niteroi, Brazil.","DOI":"10.1109\/SIBGRAPI-T.2017.12"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Shafique, S., and Tehsin, S. (2018). Acute Lymphoblastic Leukemia Detection and Classification of Its Subtypes Using Pretrained Deep Convolutional Neural Networks. Technol. Cancer Res. Treat., 17.","DOI":"10.1177\/1533033818802789"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1310","DOI":"10.1002\/jemt.23139","article-title":"Classification of acute lymphoblastic leukemia using deep learning","volume":"81","author":"Rehman","year":"2018","journal-title":"Microsc. Res. Tech."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Mohamed Loey, M.N., and Zayed, H. (2020). Deep Transfer Learning in Diagnosing Leukemia in Blood Cells. Computers, 9.","DOI":"10.3390\/computers9020029"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"7519603","DOI":"10.1155\/2019\/7519603","article-title":"Convolutional Neural Networks for Recognition of Lymphoblast Cell Images","volume":"2019","author":"Pansombut","year":"2019","journal-title":"Comput. Intell. Neurosci."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Ahmed, N., Yigit, A., Isik, Z., and Alpkocak, A. (2019). Identification of Leukemia Subtypes from Microscopic Images Using Convolutional Neural Network. Diagnostics, 9.","DOI":"10.3390\/diagnostics9030104"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"995","DOI":"10.1109\/JSYST.2014.2308452","article-title":"Automated Screening System for Acute Myelogenous Leukemia Detection in Blood Microscopic Images","volume":"8","author":"Madhukar","year":"2014","journal-title":"IEEE Syst. J."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"415","DOI":"10.1016\/j.engappai.2018.04.024","article-title":"Leukemia diagnosis in blood slides using transfer learning in CNNs and SVM for classification","volume":"72","author":"Vogado","year":"2018","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"2536","DOI":"10.1038\/s41598-020-59215-9","article-title":"Efficient Classification of White Blood Cell Leukemia with Improved Swarm Optimization of Deep Features","volume":"10","author":"Sahlol","year":"2020","journal-title":"Sci. Rep."},{"key":"ref_24","unstructured":"De Mello, R.F., and Ponti, M.A. (2018). Machine Learning: A Practical Approach on the Statistical Learning Theory, Springer."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Kornblith, S., Shlens, J., and Le, Q.V. (2019, January 15\u201320). Do Better Imagenet Models Transfer Better?. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00277"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Labati, R.D., Piuri, V., and Scotti, F. (2011, January 11\u201314). ALL-IDB: The Acute Lymphoblastic Leukemia Image Database for Image Processing. Proceedings of the 18th IEEE International Conference on Image Processing (ICIP), Brussels, Belgium.","DOI":"10.1109\/ICIP.2011.6115881"},{"key":"ref_27","first-page":"79","article-title":"Nucleus and cytoplasm segmentation in microscopic images using K means clustering and region growing","volume":"4","author":"Sarrafzadeh","year":"2015","journal-title":"Adv. Biomed. Res."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"29","DOI":"10.4103\/2153-3539.100154","article-title":"Experience with CellaVision DM96 for peripheral blood differentials in a large multi-center academic hospital system","volume":"3","author":"Raval","year":"2012","journal-title":"J. Pathol. Inform."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Sarrafzadeh, O., Rabbani, H., Talebi, A., and Banaem, H.U. (2014, January 16\u201317). Selection of the best features for leukocytes classification in blood smear microscopic images. Proceedings of the Medical Imaging 2014: Digital Pathology. International Society for Optics and Photonics, San Diego, CA, USA.","DOI":"10.1117\/12.2043605"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Sarrafzadeh, O., Rabbani, H., Dehnavi, A.M., and Talebi, A. (2015, January 27\u201330). Detecting different sub-types of acute myelogenous leukemia using dictionary learning and sparse representation. Proceedings of the 2015 IEEE International Conference on Image Processing (ICIP), Quebec City, QC, Canada.","DOI":"10.1109\/ICIP.2015.7351422"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"341","DOI":"10.1590\/1517-3151.0626","article-title":"Automatic segmentation and classification of blood components in microscopic images using a fuzzy approach","volume":"30","author":"Vale","year":"2014","journal-title":"Rev. Bras. Eng. Biomed."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"231","DOI":"10.1007\/s00292-007-0935-5","article-title":"Pathologie-Websites im World Wide Web","volume":"29","year":"2008","journal-title":"Der Pathol."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/j.micron.2018.01.010","article-title":"Fast and robust segmentation of white blood cell images by self-supervised learning","volume":"107","author":"Zheng","year":"2018","journal-title":"Micron"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Duggal, R., Gupta, A., Gupta, R., and Mallick, P. (2017, January 11\u201313). SD Layer: Stain Deconvolutional Layer for CNNs in Medical Microscopic Imaging. Proceedings of the Medical Image Computing and Computer Assisted Intervention (MICCAI 2017), Quebec City, QC, Canada.","DOI":"10.1007\/978-3-319-66179-7_50"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"333","DOI":"10.1016\/j.compmedimag.2011.01.003","article-title":"Automatic recognition of five types of white blood cells in peripheral blood","volume":"35","author":"Rezatofighi","year":"2011","journal-title":"Comput. Med. Imaging Graph."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1186\/s40537-019-0197-0","article-title":"A survey on Image Data Augmentation for Deep Learning","volume":"6","author":"Shorten","year":"2019","journal-title":"J. Big Data"},{"key":"ref_37","unstructured":"Perez, L., and Wang, J. (2017). The Effectiveness of Data Augmentation in Image Classification using Deep Learning. arXiv."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","article-title":"ImageNet Large Scale Visual Recognition Challenge","volume":"115","author":"Russakovsky","year":"2015","journal-title":"Int. J. Comput. Vis. (IJCV)"},{"key":"ref_39","unstructured":"Yosinski, J., Clune, J., Bengio, Y., and Lipson, H. (2014, January 8\u201313). How Transferable Are Features in Deep Neural Networks?. Proceedings of the 27th International Conference on Neural Information Processing Systems, Montreal, QC, Canada."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Cavallari, G., Ribeiro, L., and Ponti, M. (November, January 29). Unsupervised representation learning using convolutional and stacked auto-encoders: A domain and cross-domain feature space analysis. Proceedings of the 2018 31st SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI), Parana, Brazil.","DOI":"10.1109\/SIBGRAPI.2018.00063"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1299","DOI":"10.1109\/TMI.2016.2535302","article-title":"Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning?","volume":"35","author":"Tajbakhsh","year":"2016","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1016\/j.jvcir.2018.04.004","article-title":"Convolutional neural networks: Ensemble modeling, fine-tuning and unsupervised semantic localization for neurosurgical CLE images","volume":"54","author":"Izadyyazdanabadi","year":"2018","journal-title":"J. Vis. Commun. Image Represent."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/j.eswa.2018.05.015","article-title":"Reverse image search for scientific data within and beyond the visible spectrum","volume":"109","author":"Araujo","year":"2018","journal-title":"Expert Syst. Appl."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"407","DOI":"10.1016\/j.jvcir.2019.02.035","article-title":"Generalization of feature embeddings transferred from different video anomaly detection domains","volume":"60","author":"Ribeiro","year":"2019","journal-title":"J. Vis. Commun. Image Represent."},{"key":"ref_45","unstructured":"Simonyan, K., and Zisserman, A. (2014). Very Deep Convolutional Networks for Large-Scale Image Recognition. arXiv."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep Residual Learning for Image Recognition. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z. (2016, January 27\u201330). Rethinking the Inception Architecture for Computer Vision. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition CVPR 2016, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.308"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Chollet, F. (2017, January 21\u201326). Xception: Deep Learning with Depthwise Separable Convolutions. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.195"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"1633","DOI":"10.1109\/JBHI.2017.2705583","article-title":"DeepPap: Deep Convolutional Networks for Cervical Cell Classification","volume":"21","author":"Zhang","year":"2017","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Diaz-Pinto, A., Morales, S., Naranjo, V., K\u00f6hler, T., Mossi, J.M., and Navea, A. (2019). CNNs for automatic glaucoma assessment using fundus images: An extensive validation. Biomed. Eng. Online, 18.","DOI":"10.1186\/s12938-019-0649-y"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"442","DOI":"10.1016\/0005-2795(75)90109-9","article-title":"Comparison of the predicted and observed secondary structure of T4 phage lysozyme","volume":"405","author":"Matthews","year":"1975","journal-title":"Biochim. Biophys. Acta Protein Struct."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1016\/j.media.2017.07.004","article-title":"Designing image segmentation studies: Statistical power, sample size and reference standard quality","volume":"42","author":"Gibson","year":"2017","journal-title":"Med. Image Anal."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Sipes, R., and Li, D. (2018, January 28\u201330). Using Convolutional Neural Networks for Automated Fine Grained Image Classification of Acute Lymphoblastic Leukemia. Proceedings of the 2018 3rd International Conference on Computational Intelligence and Applications (ICCIA), Hong Kong, China.","DOI":"10.1109\/ICCIA.2018.00036"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1016\/j.neunet.2020.08.016","article-title":"Learning image features with fewer labels using a semi-supervised deep convolutional network","volume":"132","author":"Zor","year":"2020","journal-title":"Neural Netw."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Ribeiro, M.G., Neves, L.A., Roberto, G.F., Tosta, T.A.A., Martins, A.S., and do Nascimento, M.Z. (November, January 29). Analysis of the Influence of Color Normalization in the Classification of Non-Hodgkin Lymphoma Images. Proceedings of the 2018 31st SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI), Paran\u00e1, Brazil.","DOI":"10.1109\/SIBGRAPI.2018.00054"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"300","DOI":"10.3389\/fbioe.2019.00300","article-title":"Assessing the Impact of Color Normalization in Convolutional Neural Network-Based Nuclei Segmentation Frameworks","volume":"7","author":"Pontalba","year":"2019","journal-title":"Front. Bioeng. Biotechnol."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/9\/2989\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:52:18Z","timestamp":1760161938000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/9\/2989"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,4,24]]},"references-count":56,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2021,5]]}},"alternative-id":["s21092989"],"URL":"https:\/\/doi.org\/10.3390\/s21092989","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,4,24]]}}}