{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,28]],"date-time":"2026-08-28T05:16:38Z","timestamp":1787894198727,"version":"build-2784847793"},"reference-count":45,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2026,8,26]],"date-time":"2026-08-26T00:00:00Z","timestamp":1787702400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002643","name":"Kwangwoon University","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100002643","id-type":"DOI","asserted-by":"crossref"}]},{"id":[{"id":"https:\/\/ror.org\/02e9zc863","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Bioengineering"],"abstract":"<jats:p>Although ConvNeXtV2 has shown promising performance in medical image classification, approaches relying primarily on final-stage features may underutilize low-level structural and complementary hierarchical information. To address this limitation, we propose an Adaptive Multi-Layer Feature Fusion Network (AMFF-Net) based on ConvNeXtV2-B for medical image classification. The proposed framework employs a Feature Alignment (FA) module to project multi-stage features into a unified representation space and an Adaptive Multi-Layer Feature Fusion (AMFF) module to compute a single input-dependent scalar weight for each stage and dynamically adjust the relative contributions of hierarchical features. An Efficient Channel Attention (ECA) module is subsequently incorporated to enhance the fused representation through lightweight channel-wise recalibration. AMFF-Net was evaluated on three medical image classification datasets: Kvasir-v2, HAM10000, and ChestXray14. Experimental results demonstrate that AMFF-Net consistently improves classification performance over the baseline ConvNeXtV2-B and achieves competitive performance compared with representative convolutional neural network (CNN)- and Transformer-based architectures, while incurring relatively modest additional computational overhead. Ablation results further support the contribution of FA, AMFF, and ECA to the overall performance of the proposed framework.<\/jats:p>","DOI":"10.3390\/bioengineering13090983","type":"journal-article","created":{"date-parts":[[2026,8,26]],"date-time":"2026-08-26T15:04:32Z","timestamp":1787756672000},"page":"983","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["AMFF-Net: An Adaptive Multi-Layer Feature Fusion Network Based on ConvNeXtV2-B for Medical Image Classification"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-5473-9392","authenticated-orcid":false,"given":"Min-Seo","family":"Kim","sequence":"first","affiliation":[{"name":"Department of Electronic Convergence Engineering, Kwangwoon University, 20 Gwangun-ro, Nowon-gu, Seoul 01897, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hyoung-Gook","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Electronic Convergence Engineering, Kwangwoon University, 20 Gwangun-ro, Nowon-gu, Seoul 01897, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,8,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.media.2017.07.005","article-title":"A survey on deep learning in medical image analysis","volume":"42","author":"Litjens","year":"2017","journal-title":"Med. Image Anal."},{"key":"ref_2","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_3","first-page":"5998","article-title":"Attention is all you need","volume":"30","author":"Vaswani","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., and Xie, S. (2022, January 18\u201324). A ConvNet for the 2020s. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.01167"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Woo, S., Debnath, S., Hu, R., Chen, X., Liu, Z., Kweon, I.S., and Xie, S. (2023, January 17\u201324). ConvNeXt V2: Co-designing and scaling ConvNets with masked autoencoders. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Vancouver, BC, Canada.","DOI":"10.1109\/CVPR52729.2023.01548"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Pogorelov, K., Randel, K.R., Griwodz, C., Eskeland, S.L., de Lange, T., Johansen, D., Spampinato, C., Dang-Nguyen, D.T., Lux, M., and Schmidt, P.T. (2017, January 20\u201323). Kvasir: A multi-class image dataset for computer aided gastrointestinal disease detection. Proceedings of the 8th ACM Multimedia Systems Conference, Taipei, Taiwan.","DOI":"10.1145\/3083187.3083212"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"180161","DOI":"10.1038\/sdata.2018.161","article-title":"The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions","volume":"5","author":"Tschandl","year":"2018","journal-title":"Sci. Data"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Wang, X., Peng, Y., Lu, L., Lu, Z., Bagheri, M., and Summers, R.M. (2017, January 21\u201326). ChestX-ray8: Hospital-scale chest X-ray database and benchmarks on weakly supervised classification and localization of common thorax diseases. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.369"},{"key":"ref_9","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 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_10","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 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref_11","unstructured":"Tan, M., and Le, Q.V. (2021, January 18\u201324). EfficientNetV2: Smaller models and faster training. Proceedings of the 38th International Conference on Machine Learning (ICML), Virtual Event."},{"key":"ref_12","unstructured":"Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., and Gelly, S. (2021, January 3\u20137). An image is worth 16 \u00d7 16 words: Transformers for image recognition at scale. Proceedings of the 9th International Conference on Learning Representations (ICLR), Virtual Event."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., and Guo, B. (2021, January 10\u201317). Swin transformer: Hierarchical vision transformer using shifted windows. Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), Montreal, QC, Canada.","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"He, K., Chen, X., Xie, S., Li, Y., Doll\u00e1r, P., and Girshick, R. (2022, January 18\u201324). Masked autoencoders are scalable vision learners. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.01553"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"105534","DOI":"10.1016\/j.bspc.2023.105534","article-title":"HiFuse: Hierarchical multi-scale feature fusion network for medical image classification","volume":"87","author":"Huo","year":"2024","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_16","first-page":"5313","article-title":"Research on multi-scale feature fusion network algorithm based on brain tumor medical image classification","volume":"79","author":"Zhou","year":"2024","journal-title":"Comput. Mater. Contin."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Lin, T.-Y., Doll\u00e1r, P., Girshick, R., He, K., Hariharan, B., and Belongie, S. (2017). Feature pyramid networks for object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Institute of Electrical and Electronics Engineers.","DOI":"10.1109\/CVPR.2017.106"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Liu, S., Qi, L., Qin, H., Shi, J., and Jia, J. (2018, January 18\u201322). Path aggregation network for instance segmentation. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00913"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Tan, M., Pang, R., and Le, Q.V. (2020, January 13\u201319). EfficientDet: Scalable and efficient object detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01079"},{"key":"ref_20","first-page":"3","article-title":"UNet++: A nested U-Net architecture for medical image segmentation","volume":"Volume 11045","author":"Stoyanov","year":"2018","journal-title":"Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1109\/TMBMC.2021.3099367","article-title":"A novel multi-stage residual feature fusion network for detection of COVID-19 in chest X-ray images","volume":"8","author":"Fang","year":"2022","journal-title":"IEEE Trans. Mol. Biol. Multi-Scale Commun."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1016\/j.inffus.2021.02.013","article-title":"COVID-19 and non-COVID-19 classification using multi-layers fusion from lung ultrasound images","volume":"72","author":"Muhammad","year":"2021","journal-title":"Inf. Fusion"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"102324","DOI":"10.1016\/j.jocs.2024.102324","article-title":"LiteFusionNet: Boosting the performance for medical image classification with an intelligent and lightweight feature fusion network","volume":"80","author":"Asif","year":"2024","journal-title":"J. Comput. Sci."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., and Sun, G. (2018, January 18\u201322). Squeeze-and-excitation networks. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00745"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1007\/978-3-030-01234-2_1","article-title":"CBAM: Convolutional block attention module","volume":"Volume 11211","author":"Ferrari","year":"2018","journal-title":"Computer Vision\u2014ECCV 2018"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Wang, Q., Wu, B., Zhu, P., Li, P., Zuo, W., and Hu, Q. (2020, January 13\u201319). ECA-Net: Efficient channel attention for deep convolutional neural networks. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01155"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"818","DOI":"10.1007\/978-3-319-10590-1_53","article-title":"Visualizing and understanding convolutional networks","volume":"Volume 8689","author":"Fleet","year":"2014","journal-title":"Computer Vision\u2014ECCV 2014"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Hariharan, B., Arbel\u00e1ez, P., Girshick, R., and Malik, J. (2015, January 7\u201312). Hypercolumns for object segmentation and fine-grained localization. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298642"},{"key":"ref_29","first-page":"4898","article-title":"Understanding the effective receptive field in deep convolutional neural networks","volume":"29","author":"Luo","year":"2016","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Yu, F., Wang, D., Shelhamer, E., and Darrell, T. (2018, January 18\u201322). Deep layer aggregation. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00255"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Chollet, F. (2017, January 21\u201326). Xception: Deep learning with depthwise separable convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.195"},{"key":"ref_32","unstructured":"Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., and Adam, H. (2017). MobileNets: Efficient convolutional neural networks for mobile vision applications. arXiv."},{"key":"ref_33","unstructured":"Ba, J.L., Kiros, J.R., and Hinton, G.E. (2016). Layer normalization. arXiv."},{"key":"ref_34","unstructured":"Lin, M., Chen, Q., and Yan, S. (2014, January 14\u201316). Network in network. Proceedings of the 2nd International Conference on Learning Representations (ICLR), Banff, AB, Canada."},{"key":"ref_35","unstructured":"Hendrycks, D., and Gimpel, K. (2016). Gaussian error linear units (GELUs). arXiv."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"6381","DOI":"10.1038\/s41598-019-42294-8","article-title":"Comparison of deep learning approaches for multi-label chest X-ray classification","volume":"9","author":"Baltruschat","year":"2019","journal-title":"Sci. Rep."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Kufel, J., Biel\u00f3wka, M., Rojek, M., Mitr\u0119ga, A., Lewandowski, P., Cebula, M., Krawczyk, D., Biel\u00f3wka, M., Kondo\u0142, D., and Bargie\u0142-\u0141\u0105czek, K. (2023). Multi-label classification of chest X-ray abnormalities using transfer learning techniques. J. Pers. Med., 13.","DOI":"10.3390\/jpm13101426"},{"key":"ref_38","unstructured":"Ge, Z., Mahapatra, D., Sedai, S., Garnavi, R., and Chakravorty, R. (2018). Chest X-rays classification: A multi-label and fine-grained problem. arXiv."},{"key":"ref_39","unstructured":"Taslimi, S., Taslimi, S., Fathi, N., Salehi, M., and Rohban, M.H. (2022). SwinCheX: Multi-label classification on chest X-ray images with transformers. arXiv."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., and Batra, D. (2017, January 22\u201329). Grad-CAM: Visual explanations from deep networks via gradient-based localization. Proceedings of the IEEE International Conference on Computer Vision (ICCV), Venice, Italy.","DOI":"10.1109\/ICCV.2017.74"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"The Paris Workshop Participants (2003). The Paris endoscopic classification of superficial neoplastic lesions: Esophagus, stomach, and colon. Gastrointest. Endosc., 58, S3\u2013S43.","DOI":"10.1016\/S0016-5107(03)02159-X"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"599","DOI":"10.1053\/j.gastro.2012.05.006","article-title":"Validation of a simple classification system for endoscopic diagnosis of small colorectal polyps using narrow-band imaging","volume":"143","author":"Hewett","year":"2012","journal-title":"Gastroenterology"},{"key":"ref_43","unstructured":"American Academy of Dermatology Association (2026, July 28). What to Look for: ABCDEs of Melanoma. Available online: https:\/\/www.aad.org\/public\/diseases\/skin-cancer\/find\/at-risk\/abcdes."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"362","DOI":"10.5114\/pdia.2014.47119","article-title":"Dermoscopic and clinical features of trunk melanomas","volume":"31","author":"Emiroglu","year":"2014","journal-title":"Post\u0119py Dermatol. Alergol."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Truszkiewicz, K., Por\u0119ba, R., and Ga\u0107, P. (2021). Radiological cardiothoracic ratio in evidence-based medicine. J. Clin. Med., 10.","DOI":"10.3390\/jcm10092016"}],"container-title":["Bioengineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2306-5354\/13\/9\/983\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,28]],"date-time":"2026-08-28T04:17:06Z","timestamp":1787890626000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2306-5354\/13\/9\/983"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8,26]]},"references-count":45,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2026,9]]}},"alternative-id":["bioengineering13090983"],"URL":"https:\/\/doi.org\/10.3390\/bioengineering13090983","relation":{},"ISSN":["2306-5354"],"issn-type":[{"value":"2306-5354","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,8,26]]}}}