{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,27]],"date-time":"2025-12-27T05:22:29Z","timestamp":1766812949584,"version":"3.48.0"},"reference-count":58,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2025,12,25]],"date-time":"2025-12-25T00:00:00Z","timestamp":1766620800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100006319","name":"Centre National pour la Recherche Scientifique et Technique","doi-asserted-by":"publisher","award":["ALKHAWARIZMI\/2020\/20"],"award-info":[{"award-number":["ALKHAWARIZMI\/2020\/20"]}],"id":[{"id":"10.13039\/501100006319","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["MAKE"],"abstract":"<jats:p>Encoder\u2013decoder models are widely used for pixel-level segmentation due to their ability to capture and combine multiscale features. However, skip connections between the encoder and decoder often require cropping to mitigate border pixel loss during convolutions, which can introduce inefficiencies and limit performance. This study explores the potential of modifying these connections by removing direct encoder-to-decoder links to enhance segmentation accuracy. We propose a novel architecture, termed XCC-Net, which features two context-capturing pathways and two symmetric pathways for enlargement. These pathways are interconnected via channels, enabling automated detection of structures with varied shapes. The XCC-Net\u2019s X-shaped architecture links skip connections exclusively between encoder-to-encoder and decoder-to-decoder, omitting direct encoder-to-decoder feature transfers to potentially improve performance. The XCC-Net model was evaluated on multiple medical imaging datasets, including wireless capsule endoscopy (WCE), colonoscopy, and dermoscopy images. Experimental results showed that XCC-Net outperformed state-of-the-art segmentation models, achieving dice coefficients of 91.70%, 89.26%, 87.15%, and 79.07% on the MICCAI 2017 (Red Lesion), PH2, CVC-ClinicDB, and ISIC 2017 datasets, respectively. XCC-Net\u2019s X-shaped architecture, with its unique skip connections, demonstrates improved segmentation performance across various medical imaging tasks.<\/jats:p>","DOI":"10.3390\/make8010003","type":"journal-article","created":{"date-parts":[[2025,12,26]],"date-time":"2025-12-26T00:50:21Z","timestamp":1766710221000},"page":"3","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["XCC-Net: An X-Shaped Collective Convolution Network Architecture for Medical Image Segmentation"],"prefix":"10.3390","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8526-3955","authenticated-orcid":false,"given":"Anass","family":"Garbaz","sequence":"first","affiliation":[{"name":"Laboratory of Computer Systems and Vision, Department of Computer Science, Faculty of Sciences, Ibn Zohr University, Agadir 80000, Morocco"}]},{"given":"Yassine","family":"Oukdach","sequence":"additional","affiliation":[{"name":"Laboratory of Computer Systems and Vision, Department of Computer Science, Faculty of Sciences, Ibn Zohr University, Agadir 80000, Morocco"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2059-6660","authenticated-orcid":false,"given":"Said","family":"Charfi","sequence":"additional","affiliation":[{"name":"Laboratory of Computer Systems and Vision, Department of Computer Science, Faculty of Sciences, Ibn Zohr University, Agadir 80000, Morocco"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5394-9066","authenticated-orcid":false,"given":"Mohamed","family":"El Ansari","sequence":"additional","affiliation":[{"name":"Informatics and Applications Laboratory, Department of Computer Science Faculty of Sciences, My Ismail University, Meknes 50000, Morocco"}]},{"given":"Lahcen","family":"Koutti","sequence":"additional","affiliation":[{"name":"Laboratory of Computer Systems and Vision, Department of Computer Science, Faculty of Sciences, Ibn Zohr University, Agadir 80000, Morocco"}]},{"given":"Mustapha","family":"Hedabou","sequence":"additional","affiliation":[{"name":"College of Computing, Mohammed VI Polytechnic University (UM6P), Ben Guerir 43150, Morocco"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6153-7459","authenticated-orcid":false,"given":"Mustapha","family":"Oujaoura","sequence":"additional","affiliation":[{"name":"Mathematics, Informatics & Communication Systems Laboratory (MISCOM), National School of Applied Sciences of Safi, Cadi Ayyad University, Marrakech 40000, Morocco"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-9196-1166","authenticated-orcid":false,"given":"Abdel Motalib","family":"Lagsoun","sequence":"additional","affiliation":[{"name":"Mathematics, Informatics & Communication Systems Laboratory (MISCOM), National School of Applied Sciences of Safi, Cadi Ayyad University, Marrakech 40000, Morocco"}]}],"member":"1968","published-online":{"date-parts":[[2025,12,25]]},"reference":[{"key":"ref_1","first-page":"3523","article-title":"Image segmentation using deep learning: A survey","volume":"44","author":"Minaee","year":"2021","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"582","DOI":"10.1007\/s10278-019-00227-x","article-title":"Deep learning techniques for medical image segmentation: Achievements and challenges","volume":"32","author":"Hesamian","year":"2019","journal-title":"J. Digit. Imaging"},{"key":"ref_3","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, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"ref_4","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015, January 5\u20139). U-net: Convolutional networks for biomedical image segmentation. Proceedings of the Medical Image Computing and Computer-Assisted Intervention\u2013MICCAI 2015: 18th International Conference, Munich, Germany. Proceedings, part III 18."},{"key":"ref_5","first-page":"17","article-title":"Cancer statistics, 2023","volume":"73","author":"Siegel","year":"2023","journal-title":"CA Cancer J. Clin."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"645","DOI":"10.1016\/j.pop.2015.07.006","article-title":"Skin cancer","volume":"42","author":"Linares","year":"2015","journal-title":"Prim. Care Clin. Off. Pract."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"15003","DOI":"10.1038\/nrdp.2015.3","article-title":"Melanoma","volume":"1","author":"Schadendorf","year":"2015","journal-title":"Nat. Rev. Dis. Prim."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"635","DOI":"10.1016\/j.clindermatol.2021.03.009","article-title":"Dermatoscopy","volume":"39","author":"Ring","year":"2021","journal-title":"Clin. Dermatol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1093\/gastro\/goab010","article-title":"Gastrointestinal cancers in China, the USA, and Europe","volume":"9","author":"Xie","year":"2021","journal-title":"Gastroenterol. Rep."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"467","DOI":"10.4291\/wjgp.v5.i4.467","article-title":"Diagnosis of gastrointestinal bleeding: A practical guide for clinicians","volume":"5","author":"Kim","year":"2014","journal-title":"World J. Gastrointest. Pathophysiol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"745","DOI":"10.1016\/j.gie.2011.07.025","article-title":"Complications of colonoscopy","volume":"74","author":"Fisher","year":"2011","journal-title":"Gastrointest. Endosc."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"417","DOI":"10.1038\/35013140","article-title":"Wireless capsule endoscopy","volume":"405","author":"Iddan","year":"2000","journal-title":"Nature"},{"key":"ref_13","unstructured":"Coelho, P., Pereira, A., Leite, A., Salgado, M., and Cunha, A. (2018, January 27\u201329). A deep learning approach for red lesions detection in video capsule endoscopies. Proceedings of the Image Analysis and Recognition: 15th International Conference, ICIAR 2018, P\u00f3voa de Varzim, Portugal. Proceedings 15."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Codella, N.C., Gutman, D., Celebi, M.E., Helba, B., Marchetti, M.A., Dusza, S.W., Kalloo, A., Liopyris, K., Mishra, N., and Kittler, H. (2018, January 4\u20137). Skin lesion analysis toward melanoma detection: A challenge at the 2017 international symposium on biomedical imaging (isbi), hosted by the international skin imaging collaboration (isic). Proceedings of the 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), Washington, DC, USA.","DOI":"10.1109\/ISBI.2018.8363547"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Mendon\u00e7a, T., Ferreira, P.M., Marques, J.S., Marcal, A.R., and Rozeira, J. (2013, January 3\u20137). PH 2-A dermoscopic image database for research and benchmarking. Proceedings of the 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Osaka, Japan.","DOI":"10.1109\/EMBC.2013.6610779"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1016\/j.compmedimag.2015.02.007","article-title":"WM-DOVA maps for accurate polyp highlighting in colonoscopy: Validation vs. saliency maps from physicians","volume":"43","author":"Bernal","year":"2015","journal-title":"Comput. Med. Imaging Graph."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1856","DOI":"10.1109\/TMI.2019.2959609","article-title":"Unet++: Redesigning skip connections to exploit multiscale features in image segmentation","volume":"39","author":"Zhou","year":"2019","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_18","unstructured":"Oktay, O., Schlemper, J., Folgoc, L.L., Lee, M., Heinrich, M., Misawa, K., Mori, K., McDonagh, S., Hammerla, N.Y., and Kainz, B. (2018). Attention u-net: Learning where to look for the pancreas. arXiv."},{"key":"ref_19","unstructured":"Iglovikov, V., and Shvets, A. (2018). Ternausnet: U-net with vgg11 encoder pre-trained on imagenet for image segmentation. arXiv."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1016\/j.isprsjprs.2020.01.013","article-title":"ResUNet-a: A deep learning framework for semantic segmentation of remotely sensed data","volume":"162","author":"Diakogiannis","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Lafraxo, S., Souaidi, M., El Ansari, M., and Koutti, L. (2023). Semantic segmentation of digestive abnormalities from wce images by using attresu-net architecture. Life, 13.","DOI":"10.3390\/life13030719"},{"key":"ref_22","unstructured":"Chen, J., Lu, Y., Yu, Q., Luo, X., Adeli, E., Wang, Y., Lu, L., Yuille, A.L., and Zhou, Y. (2021). Transunet: Transformers make strong encoders for medical image segmentation. arXiv."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., and Wang, M. (2022, January 23\u201327). Swin-unet: Unet-like pure transformer for medical image segmentation. Proceedings of the European Conference on Computer Vision, Tel Aviv, Israel.","DOI":"10.1007\/978-3-031-25066-8_9"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Alom, M.Z., Hasan, M., Yakopcic, C., Taha, T.M., and Asari, V.K. (2018). Recurrent residual convolutional neural network based on u-net (r2u-net) for medical image segmentation. arXiv.","DOI":"10.1109\/NAECON.2018.8556686"},{"key":"ref_25","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_26","doi-asserted-by":"crossref","first-page":"178","DOI":"10.1109\/JPROC.2019.2950506","article-title":"Wireless capsule endoscopy: A new tool for cancer screening in the colon with deep-learning-based polyp recognition","volume":"108","author":"Jia","year":"2019","journal-title":"Proc. IEEE"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Borgli, H., Stensland, H.K., and Halvorsen, P. (2025). Automatic prompt generation using class activation maps for foundational models: A polyp segmentation case study. Mach. Learn. Knowl. Extr., 7.","DOI":"10.3390\/make7010022"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"e23194","DOI":"10.1002\/ima.23194","article-title":"Feature Pyramid Network Based Spatial Attention and Cross-Level Semantic Similarity for Diseases Segmentation From Capsule Endoscopy Images","volume":"34","author":"Charfi","year":"2024","journal-title":"Int. J. Imaging Syst. Technol."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Souaidi, M., Lafraxo, S., Kerkaou, Z., El Ansari, M., and Koutti, L. (2023). A multiscale polyp detection approach for gi tract images based on improved densenet and single-shot multibox detector. Diagnostics, 13.","DOI":"10.3390\/diagnostics13040733"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"235","DOI":"10.1007\/s00779-021-01660-y","article-title":"Fine-tuned deep neural networks for polyp detection in colonoscopy images","volume":"27","author":"Ellahyani","year":"2023","journal-title":"Pers. Ubiquitous Comput."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Lafraxo, S., El Ansari, M., and Koutti, L. (2023, January 26\u201328). Gastrosegnet: Polyp segmentation using colonoscopic images based on attentionu-net architecture. Proceedings of the 2023 10th International Conference on Wireless Networks and Mobile Communications (WINCOM), Istanbul, Turkey.","DOI":"10.1109\/WINCOM59760.2023.10322931"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1800112","DOI":"10.1109\/JTEHM.2017.2756034","article-title":"CHOBS: Color histogram of block statistics for automatic bleeding detection in wireless capsule endoscopy video","volume":"6","author":"Ghosh","year":"2018","journal-title":"IEEE J. Transl. Eng. Health Med."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"101852","DOI":"10.1016\/j.compmedimag.2020.101852","article-title":"Deep transfer learning approaches for bleeding detection in endoscopy images","volume":"88","author":"Caroppo","year":"2021","journal-title":"Comput. Med Imaging Graph."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Kanakatte, A., and Ghose, A. (2021, January 1\u20135). Precise Bleeding and Red lesions localization from Capsule Endoscopy using Compact U-Net. Proceedings of the 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Virtual.","DOI":"10.1109\/EMBC46164.2021.9630301"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Bai, F., Xing, X., Shen, Y., Ma, H., and Meng, M.Q.H. (2022, January 18\u201322). Discrepancy-based active learning for weakly supervised bleeding segmentation in wireless capsule endoscopy images. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, Singapore.","DOI":"10.1007\/978-3-031-16452-1_3"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Li, S., Zhang, J., Ruan, C., and Zhang, Y. (2019, January 18\u201321). Multi-stage attention-unet for wireless capsule endoscopy image bleeding area segmentation. Proceedings of the 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), San Diego, CA, USA.","DOI":"10.1109\/BIBM47256.2019.8983292"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Hajabdollahi, M., Esfandiarpoor, R., Najarian, K., Karimi, N., Samavi, S., and Soroushmehr, S.R. (2019, January 23\u201327). Low complexity cnn structure for automatic bleeding zone detection in wireless capsule endoscopy imaging. Proceedings of the 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin, Germany.","DOI":"10.1109\/EMBC.2019.8857751"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"104789","DOI":"10.1016\/j.compbiomed.2021.104789","article-title":"A deep CNN model for anomaly detection and localization in wireless capsule endoscopy images","volume":"137","author":"Jain","year":"2021","journal-title":"Comput. Biol. Med."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"4047","DOI":"10.1109\/TMI.2020.3010102","article-title":"Zoom in lesions for better diagnosis: Attention guided deformation network for wce image classification","volume":"39","author":"Xing","year":"2020","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"102863","DOI":"10.1016\/j.media.2023.102863","article-title":"A survey on deep learning for skin lesion segmentation","volume":"88","author":"Mirikharaji","year":"2023","journal-title":"Med. Image Anal."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"958","DOI":"10.1007\/s10278-020-00343-z","article-title":"Skin lesion segmentation with improved convolutional neural network","volume":"33","year":"2020","journal-title":"J. Digit. Imaging"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"105241","DOI":"10.1016\/j.cmpb.2019.105241","article-title":"Skin lesion segmentation using high-resolution convolutional neural network","volume":"186","author":"Xie","year":"2020","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"527","DOI":"10.1109\/JBHI.2018.2859898","article-title":"Dense deconvolutional network for skin lesion segmentation","volume":"23","author":"Li","year":"2018","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"101716","DOI":"10.1016\/j.media.2020.101716","article-title":"Skin lesion segmentation via generative adversarial networks with dual discriminators","volume":"64","author":"Lei","year":"2020","journal-title":"Med. Image Anal."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1109\/TMI.2020.3027341","article-title":"Automated skin lesion segmentation via an adaptive dual attention module","volume":"40","author":"Wu","year":"2020","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"103738","DOI":"10.1016\/j.compbiomed.2020.103738","article-title":"DSNet: Automatic dermoscopic skin lesion segmentation","volume":"120","author":"Hasan","year":"2020","journal-title":"Comput. Biol. Med."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"107719","DOI":"10.1016\/j.compbiomed.2023.107719","article-title":"MSS-UNet: A Multi-Spatial-Shift MLP-based UNet for skin lesion segmentation","volume":"168","author":"Zhu","year":"2024","journal-title":"Comput. Biol. Med."},{"key":"ref_48","unstructured":"Sifre, L. (2014). Rigid-Motion Scattering for Image Classification. [Ph.D. Thesis]."},{"key":"ref_49","unstructured":"Kinga, D., and Adam, J.B. (2014). A method for stochastic optimization. arXiv."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Milletari, F., Navab, N., and Ahmadi, S.A. (2016, January 25\u201328). V-net: Fully convolutional neural networks for volumetric medical image segmentation. Proceedings of the 2016 Fourth International Conference on 3D Vision (3DV), Stanford, CA, USA.","DOI":"10.1109\/3DV.2016.79"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Jha, D., Smedsrud, P.H., Riegler, M.A., Johansen, D., De Lange, T., Halvorsen, P., and Johansen, H.D. (2019, January 9\u201311). Resunet++: An advanced architecture for medical image segmentation. Proceedings of the 2019 IEEE International Symposium on Multimedia (ISM), San Diego, CA, USA.","DOI":"10.1109\/ISM46123.2019.00049"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"6923","DOI":"10.1007\/s00500-023-09576-w","article-title":"Modified residual attention network for abnormalities segmentation and detection in WCE images","volume":"28","author":"Charfi","year":"2024","journal-title":"Soft Comput."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"105131","DOI":"10.1016\/j.bspc.2023.105131","article-title":"TransCS-Net: A hybrid transformer-based privacy-protecting network using compressed sensing for medical image segmentation","volume":"86","author":"Tang","year":"2023","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Garbaz, A., Oukdach, Y., Charfi, S., El Ansari, M., Koutti, L., and Salihoun, M. (2024, January 23\u201325). Bleeding Segmentation Based on a U-Formed Network with Separable Contextual Feature-Guided in Wireless Capsule Endoscopy Images. Proceedings of the 2024 11th International Conference on Wireless Networks and Mobile Communications (WINCOM), Leeds, UK.","DOI":"10.1109\/WINCOM62286.2024.10656577"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"106674","DOI":"10.1016\/j.bspc.2024.106674","article-title":"A parallelly contextual convolutional transformer for medical image segmentation","volume":"98","author":"Feng","year":"2024","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"102327","DOI":"10.1016\/j.media.2021.102327","article-title":"FAT-Net: Feature adaptive transformers for automated skin lesion segmentation","volume":"76","author":"Wu","year":"2022","journal-title":"Med. Image Anal."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"124179","DOI":"10.1016\/j.eswa.2024.124179","article-title":"Multi-Bottleneck progressive propulsion network for medical image semantic segmentation with integrated macro-micro dual-stage feature enhancement and refinement","volume":"252","author":"Wang","year":"2024","journal-title":"Expert Syst. Appl."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"105133","DOI":"10.1016\/j.bspc.2023.105133","article-title":"TranSEFusionNet: Deep fusion network for colorectal polyp segmentation","volume":"86","author":"Zhang","year":"2023","journal-title":"Biomed. Signal Process. 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