{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,23]],"date-time":"2026-03-23T11:32:52Z","timestamp":1774265572664,"version":"3.50.1"},"reference-count":32,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2020,9,8]],"date-time":"2020-09-08T00:00:00Z","timestamp":1599523200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001871","name":"Funda\u00e7\u00e3o para a Ci\u00eancia e a Tecnologia","doi-asserted-by":"publisher","award":["DSAIPA\/DS\/0022\/2018"],"award-info":[{"award-number":["DSAIPA\/DS\/0022\/2018"]}],"id":[{"id":"10.13039\/501100001871","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>The classification of histopathology images requires an experienced physician with years of experience to classify the histopathology images accurately. In this study, an algorithm was developed to assist physicians in classifying histopathology images; the algorithm receives the histopathology image as an input and produces the percentage of cancer presence. The primary classifier used in this algorithm is the convolutional neural network, which is a state-of-the-art classifier used in image classification as it can classify images without relying on the manual selection of features from each image. The main aim of this research is to improve the robustness of the classifier used by comparing six different first-order stochastic gradient-based optimizers to select the best for this particular dataset. The dataset used to train the classifier is the PatchCamelyon public dataset, which consists of 220,025 images to train the classifier; the dataset is composed of 60% positive images and 40% negative images, and 57,458 images to test its performance. The classifier was trained on 80% of the images and validated on the rest of 20% of the images; then, it was tested on the test set. The optimizers were evaluated based on their AUC of the ROC curve. The results show that the adaptative based optimizers achieved the highest results except for AdaGrad that achieved the lowest results.<\/jats:p>","DOI":"10.3390\/jimaging6090092","type":"journal-article","created":{"date-parts":[[2020,9,8]],"date-time":"2020-09-08T09:03:48Z","timestamp":1599555828000},"page":"92","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":73,"title":["Comparative Study of First Order Optimizers for Image Classification Using Convolutional Neural Networks on Histopathology Images"],"prefix":"10.3390","volume":"6","author":[{"given":"Ibrahem","family":"Kandel","sequence":"first","affiliation":[{"name":"Nova Information Management School (NOVA IMS), Campus de Campolide, Universidade Nova de Lisboa, 1070-312 Lisboa, Portugal"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8793-1451","authenticated-orcid":false,"given":"Mauro","family":"Castelli","sequence":"additional","affiliation":[{"name":"Nova Information Management School (NOVA IMS), Campus de Campolide, Universidade Nova de Lisboa, 1070-312 Lisboa, Portugal"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6924-6119","authenticated-orcid":false,"given":"Ale\u0161","family":"Popovi\u010d","sequence":"additional","affiliation":[{"name":"School of Economics and Business, University of Ljubljana, Kardeljeva Plo\u0161\u010dad 17, 1000 Ljubljana, Slovenia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,9,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"372","DOI":"10.5858\/2009-0678-OA.1","article-title":"Clinical examination and validation of primary diagnosis in anatomic pathology using whole slide digital images","volume":"135","author":"Drogowski","year":"2011","journal-title":"Arch. Pathol. Lab. Med."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1007\/BF00344251","article-title":"Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position","volume":"36","author":"Fukushima","year":"1980","journal-title":"Biol. Cybern."},{"key":"ref_3","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_4","doi-asserted-by":"crossref","unstructured":"Fuentes, A., Yoon, S., Kim, S.C., and Park, D.S. (2017). A Robust Deep-Learning-Based Detector for Real-Time Tomato Plant Diseases and Pests Recognition. Sensors, 17.","DOI":"10.3390\/s17092022"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1419","DOI":"10.3389\/fpls.2016.01419","article-title":"Using Deep Learning for Image-Based Plant Disease Detection","volume":"7","author":"Mohanty","year":"2016","journal-title":"Front. Plant Sci."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"883","DOI":"10.1016\/j.tplants.2018.07.004","article-title":"Deep Learning for Plant Stress Phenotyping: Trends and Future Perspectives","volume":"23","author":"Singh","year":"2018","journal-title":"Trends Plant Sci."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"John, V., Yoneda, K., Qi, B., Liu, Z., and Mita, S. (2014). Traffic light recognition in varying illumination using deep learning and saliency map. 17th International IEEE Conference on Intelligent Transportation Systems (ITSC), Institute of Electrical and Electronics Engineers (IEEE).","DOI":"10.1109\/ITSC.2014.6958056"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Tae-Hyun, H., In-Hak, J., and Seong-Ik, C. (2006). Detection of Traffic Lights for Vision-Based Car Navigation System BT-Advances in Image and Video Technology, Springer.","DOI":"10.1007\/11949534_68"},{"key":"ref_9","unstructured":"Krizhevsky, A., Sutskever, I., and Hinton, G.E. (2012). ImageNet Classification with Deep Convolutional Neural Networks. Neural Inf. Process. Syst., 25."},{"key":"ref_10","unstructured":"Simonyan, K., and Zisserman, A. (2014). Very Deep Convolutional Networks for Large-Scale Image Recognition. arXiv."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016). Deep Residual Learning for Image Recognition. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Institute of Electrical and Electronics Engineers (IEEE).","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z. (2016). Rethinking the Inception Architecture for Computer Vision. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Institute of Electrical and Electronics Engineers (IEEE).","DOI":"10.1109\/CVPR.2016.308"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Dogo, E.M., Afolabi, O.J., Nwulu, N.I., Twala, B., and Aigbavboa, C.O. (2018, January 21\u201322). Aigbavboa, A Comparative Analysis of Gradient Descent-Based Optimization Algorithms on Convolutional Neural Networks. Proceedings of the 2018 International Conference on Computational Techniques, Electronics and Mechanical Systems (CTEMS), Belgaum, India.","DOI":"10.1109\/CTEMS.2018.8769211"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Prilianti, K.R., Brotosudarmo, T.H.P., Anam, S., and Suryanto, A. (2019). Performance Comparison of the Convolutional Neural Network Optimizer for Photosynthetic Pigments Prediction on Plant Digital Image, AIP Publishing.","DOI":"10.1063\/1.5094284"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"2278","DOI":"10.1109\/5.726791","article-title":"Gradient-based learning applied to document recognition","volume":"Volume 86","author":"LeCun","year":"1998","journal-title":"Proceedings of the IEEE"},{"key":"ref_16","unstructured":"Jangid, M., and Srivastava, S. (2017, January 11\u201312). Deep ConvNet with different stochastic optimizations for handwritten devanagari character. Proceedings of the IC4S 2017, Patong Phuket, Thailand."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"012044","DOI":"10.1088\/1742-6596\/1196\/1\/012044","article-title":"Appropriate CNN Architecture and Optimizer for Vehicle Type Classification System on the Toll Road","volume":"1196","author":"Swastika","year":"2019","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"2199","DOI":"10.1001\/jama.2017.14585","article-title":"Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer","volume":"318","author":"Bejnordi","year":"2017","journal-title":"JAMA"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Veeling, B.S., Linmans, J., Winkens, J., Cohen, T., and Welling, M. (2018). Rotation Equivariant CNNs for Digital Pathology BT-Medical Image Computing and Computer Assisted Intervention\u2013MICCAI 2018, Springer.","DOI":"10.1007\/978-3-030-00934-2_24"},{"key":"ref_20","unstructured":"Kaggle (2020, September 01). PatchCamelyon. Available online: https:\/\/www.kaggle.com\/c\/histopathologic-cancer-detection\/data."},{"key":"ref_21","first-page":"92","article-title":"Evaluation of Pooling Operations in Convolutional Architectures for Object Recognition","volume":"6354","author":"Scherer","year":"2010","journal-title":"Computer Vision"},{"key":"ref_22","first-page":"2121","article-title":"Adaptive Subgradient Methods for Online Learning and Stochastic Optimization","volume":"12","author":"Duchi","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref_23","unstructured":"Hinton, G., Srivastava, N., and Swersky, K. (2020, August 24). Neural Networks for Machine Learning, Lecture 6a Overview of Mini-Batch Gradient Descent. Available online: http:\/\/www.cs.toronto.edu\/-hinton\/coursera\/lecture6\/lec6.pdf."},{"key":"ref_24","unstructured":"Kingma, D., and Ba, J. (2020, August 22). Adam: A Method for Stochastic Optimization. Available online: https:\/\/arxiv.org\/abs\/1412.6980."},{"key":"ref_25","unstructured":"Dozat, T. (2016, January 2\u20134). Incorporating Nesterov Momentum into Adam. Proceedings of the 4th International Conference on Learning Representations, Workshop Track, San Juan, Puerto Rico."},{"key":"ref_26","first-page":"1345","article-title":"A Survey on Transfer Learning","volume":"22","author":"SPan","year":"2009","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Xie, M., Jean, N., Burke, M., Lobell, D., and Ermon, S. (2016, January 12\u201317). Transfer learning from deep features for remote sensing and poverty mapping. Proceedings of the 30th AAAI Conference on Artificial Intelligence, AAAI 2016, Phoenix, AZ, USA.","DOI":"10.1609\/aaai.v30i1.9906"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A. (2015, January 7\u201312). Going deeper with convolutions. Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van Der Maaten, L., and Weinberger, K.Q. (2017, January 21\u201326). Densely Connected Convolutional Networks. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref_30","first-page":"1929","article-title":"Dropout: A Simple Way to Prevent Neural Networks from Overfitting","volume":"15","author":"Srivastava","year":"2014","journal-title":"J. Mach. Learn. Res."},{"key":"ref_31","unstructured":"Fawcett, T. (2020, August 27). ROC Graphs: Notes and Practical Considerations for Data Mining Researchers. Available online: https:\/\/www.hpl.hp.com\/techreports\/2003\/HPL-2003-4.pdf."},{"key":"ref_32","first-page":"89","article-title":"ROC Curve, Lift Chart and Calibration Plot","volume":"3","author":"Vuk","year":"2006","journal-title":"Comput. Sci."}],"container-title":["Journal of Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2313-433X\/6\/9\/92\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:07:56Z","timestamp":1760177276000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2313-433X\/6\/9\/92"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,9,8]]},"references-count":32,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2020,9]]}},"alternative-id":["jimaging6090092"],"URL":"https:\/\/doi.org\/10.3390\/jimaging6090092","relation":{},"ISSN":["2313-433X"],"issn-type":[{"value":"2313-433X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,9,8]]}}}