{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T14:36:16Z","timestamp":1791902176886,"version":"4.3.4"},"reference-count":63,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2022,7,24]],"date-time":"2022-07-24T00:00:00Z","timestamp":1658620800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Princess Nourah bint Abdulrahman University","award":["PNURSP2022R308"],"award-info":[{"award-number":["PNURSP2022R308"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Acute lymphoblastic leukemia (ALL) is a deadly cancer characterized by aberrant accumulation of immature lymphocytes in the blood or bone marrow. Effective treatment of ALL is strongly associated with the early diagnosis of the disease. Current practice for initial ALL diagnosis is performed through manual evaluation of stained blood smear microscopy images, which is a time-consuming and error-prone process. Deep learning-based human-centric biomedical diagnosis has recently emerged as a powerful tool for assisting physicians in making medical decisions. Therefore, numerous computer-aided diagnostic systems have been developed to autonomously identify ALL in blood images. In this study, a new Bayesian-based optimized convolutional neural network (CNN) is introduced for the detection of ALL in microscopic smear images. To promote classification performance, the architecture of the proposed CNN and its hyperparameters are customized to input data through the Bayesian optimization approach. The Bayesian optimization technique adopts an informed iterative procedure to search the hyperparameter space for the optimal set of network hyperparameters that minimizes an objective error function. The proposed CNN is trained and validated using a hybrid dataset which is formed by integrating two public ALL datasets. Data augmentation has been adopted to further supplement the hybrid image set to boost classification performance. The Bayesian search-derived optimal CNN model recorded an improved performance of image-based ALL classification on test set. The findings of this study reveal the superiority of the proposed Bayesian-optimized CNN over other optimized deep learning ALL classification models.<\/jats:p>","DOI":"10.3390\/s22155520","type":"journal-article","created":{"date-parts":[[2022,7,25]],"date-time":"2022-07-25T04:52:47Z","timestamp":1658724767000},"page":"5520","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":71,"title":["BO-ALLCNN: Bayesian-Based Optimized CNN for Acute Lymphoblastic Leukemia Detection in Microscopic Blood Smear Images"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5462-595X","authenticated-orcid":false,"given":"Ghada","family":"Atteia","sequence":"first","affiliation":[{"name":"Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7530-7961","authenticated-orcid":false,"given":"Amel","family":"Alhussan","sequence":"additional","affiliation":[{"name":"Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5957-1383","authenticated-orcid":false,"given":"Nagwan","family":"Samee","sequence":"additional","affiliation":[{"name":"Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,7,24]]},"reference":[{"key":"ref_1","unstructured":"(2022, July 01). Common Cancer Types\u2014NCI, Available online: https:\/\/www.cancer.gov\/types\/common-cancers."},{"key":"ref_2","unstructured":"(2022, June 30). Leukemia\u2014Cancer Stat Facts, Available online: https:\/\/seer.cancer.gov\/statfacts\/html\/leuks.html."},{"key":"ref_3","first-page":"951","article-title":"Acute Leukemia: An Illustrated Guide to Diagnosis and Treatment","volume":"6","author":"Emadi","year":"2018","journal-title":"Angew. Chem. Int. Ed."},{"key":"ref_4","first-page":"9933481","article-title":"Machine Learning in Detection and Classification of Leukemia Using Smear Blood Images: A Systematic Review","volume":"2021","author":"Ghaderzadeh","year":"2021","journal-title":"Sci. Program."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"3475","DOI":"10.1109\/ACCESS.2016.2636218","article-title":"Leukocytes Classification and Segmentation in Microscopic Blood Smear: A Resource-Aware Healthcare Service in Smart Cities","volume":"5","author":"Sajjad","year":"2017","journal-title":"IEEE Access"},{"key":"ref_6","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_7","doi-asserted-by":"crossref","unstructured":"Loey, M., Naman, M., and Zayed, H. (2020). Deep Transfer Learning in Diagnosing Leukemia in Blood Cells. Computers, 9.","DOI":"10.3390\/computers9020029"},{"key":"ref_8","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_9","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1038\/s41746-020-00376-2","article-title":"Deep learning-enabled medical computer vision","volume":"4","author":"Esteva","year":"2021","journal-title":"Npj Digit. Med."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"6648574","DOI":"10.1155\/2020\/6648574","article-title":"IOMT-based automated detection and classification of leukemia using deep learning","volume":"2020","author":"Bibi","year":"2020","journal-title":"J. Healthc. Eng."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1007\/5584_2020_499","article-title":"Algorithms for Diagnosis of Diabetic Retinopathy and Diabetic Macula Edema\u2014A Review","volume":"1307","author":"Suriyasekeran","year":"2021","journal-title":"Adv. Exp. Med. Biol."},{"key":"ref_12","first-page":"1","article-title":"Deep learning for COVID-19 detection based on CT images","volume":"11","author":"Zhao","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_13","first-page":"647","article-title":"Evaluation of using Parametric and Non-parametric Machine Learning Algorithms for COVID-19 Forecasting","volume":"12","author":"Atteia","year":"2021","journal-title":"Int. J. Adv. Comput. Sci. Appl."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"2461","DOI":"10.1016\/S0140-6736(21)01358-1","article-title":"SARS-CoV-2 Delta VOC in Scotland: Demographics, risk of hospital admission, and vaccine effectiveness","volume":"397","author":"Sheikh","year":"2021","journal-title":"Lancet"},{"key":"ref_15","first-page":"126318","article-title":"A deep transfer learning method based on stacked autoencoder for cross-domain fault diagnosis","volume":"408","author":"Deng","year":"2021","journal-title":"Appl. Math. Comput."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"Lecun","year":"2015","journal-title":"Nature"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Khan, U., Khan, S., Rizwan, A., Atteia, G., Jamjoom, M.M., and Samee, N.A. (2022). Aggression Detection in Social Media from Textual Data Using Deep Learning Models. Appl. Sci., 12.","DOI":"10.3390\/app12105083"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Samee, N.A., Atteia, G., Alkanhel, R., Alhussan, A.A., and Aleisa, H.N. (2022). Hybrid Feature Reduction Using PCC-Stacked Autoencoders for Gold\/Oil Prices Forecasting under COVID-19 Pandemic. Electronics, 11.","DOI":"10.3390\/electronics11070991"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"611","DOI":"10.1007\/s13244-018-0639-9","article-title":"Convolutional neural networks: An overview and application in radiology","volume":"9","author":"Yamashita","year":"2018","journal-title":"Insights Imaging"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1038\/s41375-021-01408-w","article-title":"Deep learning detects acute myeloid leukemia and predicts NPM1 mutation status from bone marrow smears","volume":"36","author":"Eckardt","year":"2021","journal-title":"Leukemia"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Atteia, G., Samee, N.A., and Hassan, H.Z. (2021). DFTSA-Net: Deep Feature Transfer-Based Stacked Autoencoder Network for DME Diagnosis. Entropy, 23.","DOI":"10.3390\/e23101251"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1952","DOI":"10.1080\/08839514.2021.1995974","article-title":"Automatic Detection of Acute Lymphoblastic Leukemia Using UNET Based Segmentation and Statistical Analysis of Fused Deep Features","volume":"35","author":"Alagu","year":"2021","journal-title":"Appl. Artif. Intell."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Samee, N.A., Alhussan, A.A., Ghoneim, V.F., Atteia, G., Alkanhel, R., Al-antari, M.A., and Kadah, Y.M. (2022). A Hybrid Deep Transfer Learning of CNN-Based LR-PCA for Breast Lesion Diagnosis via Medical Breast Mammograms. Sensors, 22.","DOI":"10.3390\/s22134938"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"e201800488","DOI":"10.1002\/jbio.201800488","article-title":"LeukocyteMask: An automated localization and segmentation method for leukocyte in blood smear images using deep neural networks","volume":"12","author":"Fan","year":"2019","journal-title":"J. Biophotonics"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Prangemeier, T., Reich, C., and Koeppl, H. (2020, January 16\u201319). Attention-Based Transformers for Instance Segmentation of Cells in Microstructures. Proceedings of the 2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Seoul, Korea.","DOI":"10.1109\/BIBM49941.2020.9313305"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Wu, Y., Ge, Z., Zhang, D., Xu, M., Zhang, L., Xia, Y., and Cai, J. (2021). Enforcing Mutual Consistency of Hard Regions for Semi-supervised Medical Image Segmentation. Arvix, 4.","DOI":"10.1016\/j.media.2022.102530"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"237","DOI":"10.1007\/978-3-030-00934-2_27","article-title":"Panoptic segmentation with an end-to-end cell R-CNN for pathology image analysis","volume":"11071 LNCS","author":"Zhang","year":"2018","journal-title":"Lect. Notes Comput. Sci."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"108829","DOI":"10.1016\/j.patcog.2022.108829","article-title":"CVM-Cervix: A hybrid cervical Pap-smear image classification framework using CNN, visual transformer and multilayer perceptron","volume":"130","author":"Liu","year":"2022","journal-title":"Pattern Recognit."},{"key":"ref_29","first-page":"257","article-title":"Feature learning with component selective encoding for histopathology image classification","volume":"2018","author":"Song","year":"2018","journal-title":"Proc. Int. Symp. Biomed. Imaging"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.cca.2020.10.039","article-title":"Diagnosing acute promyelocytic leukemia by using convolutional neural network","volume":"512","author":"Ouyang","year":"2021","journal-title":"Clin. Chim. Acta."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Liu, D., Zhang, D., Song, Y., Zhang, F., O\u2019Donnell, L., Huang, H., Chen, M., and Cai, W. (2020). Unsupervised Instance Segmentation in Microscopy Images via Panoptic Domain Adaptation and Task Re-weighting. Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., 4242\u20134251.","DOI":"10.1109\/CVPR42600.2020.00430"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"2078","DOI":"10.2337\/dc20-3064","article-title":"A Multitask Deep-Learning System to Classify Diabetic Macular Edema for Different Optical Coherence Tomography Devices: A Multicenter Analysis","volume":"44","author":"Tang","year":"2021","journal-title":"Diabetes Care"},{"key":"ref_33","unstructured":"Goodfellow, I., Bengio, Y., and Courville, A. (2017). Deep Learning\u2014Adaptive Computation and Machine Learning, The MIT Press."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Hutter, F., Kotthoff, L., and Vanschoren, J. (2019). Automated Machine Learning, Springer International Publishing.","DOI":"10.1007\/978-3-030-05318-5"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"2319","DOI":"10.1016\/j.aej.2017.08.025","article-title":"A decision support system for Acute Leukaemia classification based on digital microscopic images","volume":"57","author":"Negm","year":"2018","journal-title":"Alex. Eng. J."},{"key":"ref_36","first-page":"147","article-title":"A Proposed Novel Method for Detection and Classification of Leukemia using Blood Microscopic Images","volume":"8","author":"Begum","year":"2017","journal-title":"Int. J. Adv. Res. Comput. Sci."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"5175","DOI":"10.1007\/s00521-018-3359-7","article-title":"Rough set theory with Jaya optimization for acute lymphoblastic leukemia classification","volume":"31","author":"Jothi","year":"2019","journal-title":"Neural Comput. Appl."},{"key":"ref_38","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":"Agaian","year":"2014","journal-title":"IEEE Syst. J."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"183","DOI":"10.4103\/2228-7477.186885","article-title":"Automatic Recognition of Acute Myelogenous Leukemia in Blood Microscopic Images Using K-means Clustering and Support Vector Machine","volume":"6","author":"Kazemi","year":"2016","journal-title":"J. Med. Signals Sens."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"49","DOI":"10.4103\/2228-7477.150428","article-title":"Recognition of acute lymphoblastic leukemia cells in microscopic images using k-means clustering and support vector machine classifier","volume":"5","author":"Amin","year":"2015","journal-title":"J. Med. Signals Sens."},{"key":"ref_41","first-page":"2031","article-title":"Diagnosis of leukemia disease based on enhanced virtual neural network","volume":"69","author":"Muthumayil","year":"2021","journal-title":"Comput. Mater. Contin."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.patrec.2019.03.024","article-title":"Acute lymphoblastic leukemia segmentation using local pixel information","volume":"125","author":"Sjarif","year":"2019","journal-title":"Pattern Recognit. Lett."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1005","DOI":"10.3389\/fbioe.2020.01005","article-title":"Automated Detection of Acute Lymphoblastic Leukemia From Microscopic Images Based on Human Visual Perception","volume":"8","author":"Bodzas","year":"2020","journal-title":"Front. Bioeng. Biotechnol."},{"key":"ref_44","first-page":"1","article-title":"A deep network designed for segmentation and classification of leukemia using fusion of the transfer learning models","volume":"1","author":"Saleem","year":"2021","journal-title":"Complex Intell. Syst."},{"key":"ref_45","unstructured":"G\u00e9ron, A. (2019). Hands-on Machine Learning with Scikit-Learn, Keras and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems, O\u2019Reilly Media."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1177\/1533033818802789","article-title":"Acute Lymphoblastic Leukemia Detection and Classification of Its Subtypes Using Pretrained Deep Convolutional Neural Networks","volume":"17","author":"Shafique","year":"2018","journal-title":"Technol. Cancer Res. Treat."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"100794","DOI":"10.1016\/j.imu.2021.100794","article-title":"Ensemble of Convolutional Neural Networks to diagnose Acute Lymphoblastic Leukemia from microscopic images","volume":"27","author":"Mondal","year":"2021","journal-title":"Inform. Med. Unlocked"},{"key":"ref_48","first-page":"4193","article-title":"Metaheuristic Optimization Through Deep Learning Classification of COVID-19 in Chest X-Ray Images","volume":"73","author":"Atteia","year":"2022","journal-title":"Comput. Mater. Contin."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Wu, C., Khishe, M., Mohammadi, M., Taher Karim, S.H., and Rashid, T.A. (2021). Evolving deep convolutional neutral network by hybrid sine\u2013cosine and extreme learning machine for real-time COVID-19 diagnosis from X-ray images. Soft Comput.","DOI":"10.1007\/s00500-021-05839-6"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"103688","DOI":"10.1016\/j.bspc.2022.103688","article-title":"Diagnose Parkinson\u2019s disease and cleft lip and palate using deep convolutional neural networks evolved by IP-based chimp optimization algorithm","volume":"77","author":"Chen","year":"2022","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"1355","DOI":"10.1007\/s11277-021-09410-2","article-title":"Pulmonary Diffuse Airspace Opacities Diagnosis from Chest X-ray Images Using Deep Convolutional Neural Networks Fine-Tuned by Whale Optimizer","volume":"124","author":"Wang","year":"2022","journal-title":"Wirel. Pers. Commun."},{"key":"ref_52","first-page":"7333","article-title":"Enhanced Deep CNN Based Arithmetic Optimization Algorithm for Acute Myelogenous Leukemia Detection | Annals of the Romanian Society for Cell Biology","volume":"251","author":"Ramya","year":"2021","journal-title":"Ann. Rom. Soc. Cell Biol."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1007\/978-3-319-63754-9_7","article-title":"Computer-Aided Acute Lymphoblastic Leukemia Diagnosis System Based on Image Analysis","volume":"730","author":"Abdeldaim","year":"2018","journal-title":"Stud. Comput. Intell."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"6345","DOI":"10.1007\/s00500-018-3288-5","article-title":"Automatic acute lymphoblastic leukemia classification model using social spider optimization algorithm","volume":"23","author":"Sahlol","year":"2019","journal-title":"Soft Comput."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"759","DOI":"10.1515\/bmt-2018-0213","article-title":"Sparse-FCM and Deep Convolutional Neural Network for the segmentation and classification of acute lymphoblastic leukaemia","volume":"65","author":"Praveena","year":"2020","journal-title":"Biomed. Tech."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2022\/7954111","article-title":"Optimal Deep Transfer Learning-Based Human-Centric Biomedical Diagnosis for Acute Lymphoblastic Leukemia Detection","volume":"2022","author":"Hamza","year":"2022","journal-title":"Comput. Intell. Neurosci."},{"key":"ref_57","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 IEEE International Conference on Image Processing (ICIP), Brussels, Belgium.","DOI":"10.1109\/ICIP.2011.6115881"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"347","DOI":"10.1007\/BF01099263","article-title":"Application of Bayesian approach to numerical methods of global and stochastic optimization","volume":"4","author":"Mockus","year":"1994","journal-title":"J. Glob. Optim."},{"key":"ref_59","unstructured":"Jasper, S., Hugo, L., and Adams, R. (2012, January 3\u20136). Practical Bayesian optimization of machine learning algorithms. Proceedings of the 25th International Conference on Neural Information Processing Systems, Lake Tahoe Nevada, CA, USA."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"105213","DOI":"10.1016\/j.compbiomed.2022.105213","article-title":"Bayesian-based optimized deep learning model to detect COVID-19 patients using chest X-ray image data","volume":"142","author":"Loey","year":"2022","journal-title":"Comput. Biol. Med."},{"key":"ref_61","unstructured":"Murphy, K.P. (2012). Machine Learning: A Probabilistic Perspective, The MIT Press."},{"key":"ref_62","unstructured":"Bishop, M.C. (2006). Pattern Recognition and Machine Learning, Springer."},{"key":"ref_63","unstructured":"Zheng, A. (2015). Evaluating Machine Learning Models, O\u2019Reilly Media, Inc."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/15\/5520\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:55:40Z","timestamp":1760140540000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/15\/5520"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,7,24]]},"references-count":63,"journal-issue":{"issue":"15","published-online":{"date-parts":[[2022,8]]}},"alternative-id":["s22155520"],"URL":"https:\/\/doi.org\/10.3390\/s22155520","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,7,24]]}}}