{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,29]],"date-time":"2026-08-29T04:47:30Z","timestamp":1787978850818,"version":"build-2784847793"},"reference-count":44,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2021,6,17]],"date-time":"2021-06-17T00:00:00Z","timestamp":1623888000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004515","name":"Universiti Kebangsaan Malaysia","doi-asserted-by":"publisher","award":["GUP-2019-008"],"award-info":[{"award-number":["GUP-2019-008"]}],"id":[{"id":"10.13039\/501100004515","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003093","name":"Ministry of Higher Education, Malaysia","doi-asserted-by":"publisher","award":["FRGS\/1\/2019\/ICT02\/ UKM\/02\/1"],"award-info":[{"award-number":["FRGS\/1\/2019\/ICT02\/ UKM\/02\/1"]}],"id":[{"id":"10.13039\/501100003093","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Pterygium is an eye condition that is prevalent among workers that are frequently exposed to sunlight radiation. However, most of them are not aware of this condition, which motivates many volunteers to set up health awareness booths to give them free health screening. As a result, a screening tool that can be operated on various platforms is needed to support the automated pterygium assessment. One of the crucial functions of this assessment is to extract the infected regions, which directly correlates with the severity levels. Hence, Group-PPM-Net is proposed by integrating a spatial pyramid pooling module (PPM) and group convolution to the deep learning segmentation network. The system uses a standard mobile phone camera input, which is then fed to a modified encoder-decoder convolutional neural network, inspired by a Fully Convolutional Dense Network that consists of a total of 11 dense blocks. A PPM is integrated into the network because of its multi-scale capability, which is useful for multi-scale tissue extraction. The shape of the tissues remains relatively constant, but the size will differ according to the severity levels. Moreover, group and shuffle convolution modules are also integrated at the decoder side of Group-PPM-Net by placing them at the starting layer of each dense block. The addition of these modules allows better correlation among the filters in each group, while the shuffle process increases channel variation that the filters can learn from. The results show that the proposed method obtains mean accuracy, mean intersection over union, Hausdorff distance, and Jaccard index performances of 0.9330, 0.8640, 11.5474, and 0.7966, respectively.<\/jats:p>","DOI":"10.3390\/diagnostics11061104","type":"journal-article","created":{"date-parts":[[2021,6,17]],"date-time":"2021-06-17T11:20:26Z","timestamp":1623928826000},"page":"1104","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":26,"title":["Group and Shuffle Convolutional Neural Networks with Pyramid Pooling Module for Automated Pterygium Segmentation"],"prefix":"10.3390","volume":"11","author":[{"given":"Siti Raihanah","family":"Abdani","sequence":"first","affiliation":[{"name":"Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4010-3990","authenticated-orcid":false,"given":"Mohd Asyraf","family":"Zulkifley","sequence":"additional","affiliation":[{"name":"Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nuraisyah Hani","family":"Zulkifley","sequence":"additional","affiliation":[{"name":"Community Health Department, Faculty of Medicine and Health Sciences, Universiti Putra Malaysia, Serdang 43400, Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,6,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"34563","DOI":"10.1007\/s11042-019-08130-x","article-title":"Pterygium-net: A deep learning approach to pterygium detection and localization","volume":"78","author":"Zulkifley","year":"2019","journal-title":"Multimed. Tools Appl."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Abdani, S.R., Zulkifley, M.A., and Hussain, A. (2019, January 17\u201319). Compact convolutional neural networks for pterygium classification using transfer learning. Proceedings of the IEEE International Conference on Signal and Image Processing Applications, Kuala Lumpur, Malaysia.","DOI":"10.1109\/ICSIPA45851.2019.8977757"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"3","DOI":"10.3892\/mmr.2016.5223","article-title":"The role of ultraviolet radiation in the pathogenesis of pterygia (review)","volume":"14","author":"Zhou","year":"2016","journal-title":"Mol. Med. Rep."},{"key":"ref_4","first-page":"45","article-title":"Ultraviolet light and pterygium","volume":"31","author":"Sekelj","year":"2007","journal-title":"Coll. Antropol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"194","DOI":"10.1016\/j.joco.2016.09.002","article-title":"The prevalence and determinants of pterygium in rural areas","volume":"29","author":"Hashemi","year":"2016","journal-title":"J. Curr. Ophthalmol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"748","DOI":"10.1136\/bjophthalmol-2017-310288","article-title":"Comparison among adjuvant treatments for primary pterygium: A network meta-analysis","volume":"102","author":"Fonseca","year":"2018","journal-title":"Br. J. Ophthalmol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"292","DOI":"10.1097\/00003226-200005000-00007","article-title":"Evaluating pterygium severity: A survey of corneal specialists","volume":"19","author":"Twelker","year":"2000","journal-title":"Cornea"},{"key":"ref_8","unstructured":"Bochkovskiy, A., Wang, C., and Liao, H.M. (2020). Yolov4: Optimal speed and accuracy of object detection. arXiv."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Jegou, S., Drozdzal, M., Vazquez, D., Romero, A., and Bengio, Y. (2017, January 21\u201326). The one hundred layers tiramisu: Fully convolutional densenets for semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Honolulu, HI, USA.","DOI":"10.1109\/CVPRW.2017.156"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Zhao, H., Shi, J., Qi, X., Wang, X., and Jia, J. (2017, January 21\u201326). Pyramid scene parsing network. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.660"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Zhang, X., Zhou, X., Lin, M., and Sun, J. (2018, January 18\u201323). Shufflenet: An extremely efficient convolutional neural network for mobile devices. Proceedings of the 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00716"},{"key":"ref_12","unstructured":"Krizhevsky, A., Sutskever, I., and Hinton, G.E. (2012). Imagenet classification with deep convolutional neural networks. NIPS\u201912: Proceedings of the 25th International Conference on Neural Information Processing Systems\u2014Volume 1, Curran Associates Inc."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Schoeffmann, K., Chalidabhongse, T.H., Ngo, C.W., Aramvith, S., O\u2019Connor, N.E., Ho, Y.-S., Gabbouj, M., and Elgammal, A. (2018). Convolution with logarithmic filter groups for efficient shallow cnn. MultiMedia Modeling, Springer International Publishing.","DOI":"10.1007\/978-3-319-73600-6"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Mesquita, R.G., and Figueiredo, E.M.N. (2012, January 25\u201330). An algorithm for measuring pterygium\u2019s progress in already diagnosed eyes. Proceedings of the 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Kyoto, Japan.","DOI":"10.1109\/ICASSP.2012.6287988"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Hanifah, U., Daru Kusuma, P., and Setianingsih, C. (2018, January 5\u20137). Detection of pterygium disease using forward chaining and viola jones algorithm. Proceedings of the 2018 International Conference on Control, Electronics, Renewable Energy and Communications (ICCEREC), Bandung, Indonesia.","DOI":"10.1109\/ICCEREC.2018.8712102"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"342","DOI":"10.1007\/s10384-018-0583-8","article-title":"Detection of increase in corneal irregularity due to pterygium using fourier series harmonic analyses with multiple diameters","volume":"62","author":"Minami","year":"2018","journal-title":"Jpn. J. Ophthalmol."},{"key":"ref_17","unstructured":"Gao, X., Wong, D.W.K., Aryaputera, A.W., Sun, Y., Cheng, C., Cheung, C., and Wong, T.Y. (September, January 28). Automatic pterygium detection on cornea images to enhance computer-aided cortical cataract grading system. Proceedings of the 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, San Diego, CA, USA."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"272","DOI":"10.1016\/j.optom.2019.04.001","article-title":"Corneo-pterygium total area measurements utilising image analysis method","volume":"12","author":"Khairidzan","year":"2019","journal-title":"J. Optom."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Tavares, J.M.R.S., and Jorge, R.M.N. (2019). Automatic classification of pterygium-non pterygium images using deep learning. VipIMAGE 2019, Springer International Publishing.","DOI":"10.1007\/978-3-030-32040-9"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"91","DOI":"10.17576\/jkukm-2020-32(1)-11","article-title":"Optimizing hopfield neural network for super-resolution mapping","volume":"32","author":"Muad","year":"2020","journal-title":"J. Kejuruter."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Abdani, S.R., Zulkifley, M.A., and Moubark, A.M. (2020, January 18\u201319). Pterygium tissues segmentation using densely connected deeplab. Proceedings of the 2020 IEEE 10th Symposium on Computer Applications Industrial Electronics (ISCAIE), Penang, Malaysia.","DOI":"10.1109\/ISCAIE47305.2020.9108822"},{"key":"ref_22","unstructured":"Simonyan, K., and Zisserman, A. (2014). Very Deep Convolutional Networks for Large-Scale Image Recognition, University of Oxford. Technical Report."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"42790","DOI":"10.1109\/ACCESS.2018.2859595","article-title":"Multiple-model fully convolutional neural networks for single object tracking on thermal infrared video","volume":"6","author":"Zulkifley","year":"2018","journal-title":"IEEE Access"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Rochan, M., Ye, L., and Wang, Y. (2018, January 8\u201314). Video summarization using fully convolutional sequence networks. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01258-8_22"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"48635","DOI":"10.1109\/ACCESS.2019.2910297","article-title":"Squat angle assessment through tracking body movements","volume":"7","author":"Zulkifley","year":"2019","journal-title":"IEEE Access"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., and Darrell, T. (2015, January 7\u201312). Fully convolutional networks for semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Chen, L.-C., Yang, Y., Wang, J., Xu, W., and Yuille, A.L. (2016, January 27\u201330). Attention to scale: Scale-aware semantic image segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.396"},{"key":"ref_28","unstructured":"Yu, F., and Koltun, V. (2016, January 2\u20134). Multi-scale context aggregation by dilated convolutions. Proceedings of the International Conference on Learning Representations (ICLR), San Juan, Puerto Rico."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Noh, H., Hong, S., and Han, B. (2015, January 7\u201313). Learning deconvolution network for semantic segmentation. Proceedings of the 2015 IEEE International Conference on Computer Vision (ICCV), Santiago, Chile.","DOI":"10.1109\/ICCV.2015.178"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"2481","DOI":"10.1109\/TPAMI.2016.2644615","article-title":"Segnet: A deep convolutional encoder-decoder architecture for image segmentation","volume":"39","author":"Badrinarayanan","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Navab, N., Hornegger, J., Wells, W.M., and Frangi, A.F. (2015). U-net: Convolutional networks for biomedical image segmentation. Medical Image Computing and Computer-Assisted Intervention\u2013MICCAI 2015, Springer International Publishing.","DOI":"10.1007\/978-3-319-24571-3"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2015). Deep residual learning for image recognition. arXiv.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"294","DOI":"10.1016\/j.ijid.2020.04.047","article-title":"Clinical analysis of 10 pregnant women with covid-19 in wuhan, china: A retrospective study","volume":"95","author":"Cao","year":"2020","journal-title":"Int. J. Infect. Dis."},{"key":"ref_34","unstructured":"Shah, S., Ghosh, P., Davis, L.S., and Goldstein, T. (2018). Stacked u-nets: A no-frills approach to natural image segmentation. arXiv."},{"key":"ref_35","unstructured":"Chen, L., Papandreou, G., Schroff, F., and Adam, H. (2017). Rethinking atrous convolution for semantic image segmentation. arXiv."},{"key":"ref_36","unstructured":"Ferrari, V., Hebert, M., Sminchisescu, C., and Weiss, Y. (2018). Encoder-decoder with atrous separable convolution for semantic image segmentation. Computer Vision\u2013ECCV 2018, Springer International Publishing."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Tai, S.-K., Dewi, C., Chen, R.-C., Liu, Y.-T., Jiang, X., and Yu, H. (2020). Deep learning for traffic sign recognition based on spatial pyramid pooling with scale analysis. Appl. Sci., 10.","DOI":"10.3390\/app10196997"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Jose, A., Lopez, R.D., Heisterklaus, I., and Wien, M. (2018, January 7\u201310). Pyramid Pooling of Convolutional Feature Maps for Image Retrieval. Proceedings of the 2018 25th International Conference on Image Processing (ICIP), Athens, Greece.","DOI":"10.1109\/ICIP.2018.8451361"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Abdani, S.R., Zulkifley, M.A., Siham, M.N., Abiddin, N.Z., and Aziz, N.A.A. (2020, January 9\u201311). Paddy Fields Segmentation using Fully Convolutional Network with Pyramid Pooling Module. Proceedings of the 2020 IEEE 5th International Symposium on Telecommunication Technologies (ISTT), Shah Alam, Malaysia.","DOI":"10.1109\/ISTT50966.2020.9279341"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Zhu, R., Mao, X., Zhu, Q., Li, N., and Yang, Y. (2016, January 25\u201328). Text detection based on convolutional neural networks with spatial pyramid pooling. Proceedings of the 2016 IEEE International Conference on Image Processing (ICIP), Phoenix, AZ, USA.","DOI":"10.1109\/ICIP.2016.7532514"},{"key":"ref_41","unstructured":"Ioannou, Y. (2021, May 23). A Tutorial on Filter Groups (Grouped Convolution). Available online: https:\/\/blog.yani.io\/filter-group-tutorial\/."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"2514","DOI":"10.1109\/TMI.2018.2837502","article-title":"Deep Learning Techniques for Automatic MRI Cardiac Multi-Structures Segmentation and Diagnosis: Is the Problem Solved?","volume":"37","author":"Bernard","year":"2018","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Dewi, C., Chen, R.C., Liu, Y.T., and Tai, S.K. (2021). Synthetic Data generation using DCGAN for improved traffic sign recognition. Neural Comput. Appl., 1\u201316.","DOI":"10.1007\/s00521-021-05982-z"},{"key":"ref_44","unstructured":"Kingma, D.P., and Ba, J. (2014, January 14\u201316). Adam: A method for stochastic optimization. Proceedings of the 3rd International Conference on Learning Representations, Banff, AB, Canada."}],"container-title":["Diagnostics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-4418\/11\/6\/1104\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:17:46Z","timestamp":1760163466000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-4418\/11\/6\/1104"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,6,17]]},"references-count":44,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2021,6]]}},"alternative-id":["diagnostics11061104"],"URL":"https:\/\/doi.org\/10.3390\/diagnostics11061104","relation":{},"ISSN":["2075-4418"],"issn-type":[{"value":"2075-4418","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,6,17]]}}}