{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,27]],"date-time":"2026-10-27T18:32:48Z","timestamp":1793125968851,"version":"4.5.0"},"reference-count":41,"publisher":"Wiley","issue":"2","license":[{"start":{"date-parts":[[2025,8,30]],"date-time":"2025-08-30T00:00:00Z","timestamp":1756512000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2025,8,30]],"date-time":"2025-08-30T00:00:00Z","timestamp":1756512000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["analyticalsciencejournals.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Microscopy Res &amp; Technique"],"published-print":{"date-parts":[[2026,2]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>\n                    Identification of chromosome pairs is important for karyotype generation and genetic disease prediction. Conventional methods tend to fail with structural variations of the chromosomes and imprecise boundaries. To solve these issues, we propose a deep learning architecture, Edge Fusion Attention Network (EFANet), for chromosome classification. It is a new architecture that combines the Adaptive Edge Preserve Fusion (AEPF) algorithm with the Feature Focused Attention Network (F\n                    <jats:sup>2<\/jats:sup>\n                    ANet). The AEPF algorithm clearly identifies chromosome boundaries and highlights the morphological differences. It improves feature representation by combining edge features with intensity, thereby ensuring accurate classification. The F\n                    <jats:sup>2<\/jats:sup>\n                    ANet block in EFANet improves classification using three main components: a feature extraction block, an attention block with both channel and spatial attention, and a classification block. Our proposed method ensures accurate chromosome classification, which is essential for diagnosing genetic disorders such as aneuploidies and translocations. Edge detection, a key feature of EFANet, enhances the identification of chromosome abnormalities by focusing more on unusual shapes than normal ones. Our proposed EFANet showed strong performance with 99.5% accuracy, 99.48% F1 score, 99.63% precision, and 99.45% recall. These results highlight its effectiveness in edge detection, which is important for improving automated chromosome analysis. This approach tremendously improves karyotyping by overcoming the limitations of the traditional approaches, resulting in more accurate and timely genetic disease diagnosis and eventually better patient outcomes.\n                  <\/jats:p>","DOI":"10.1002\/jemt.70070","type":"journal-article","created":{"date-parts":[[2025,8,30]],"date-time":"2025-08-30T12:40:14Z","timestamp":1756557614000},"page":"230-242","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Chromosome Image Classification Using Edge Fusion Attention Network"],"prefix":"10.1002","volume":"89","author":[{"given":"V.","family":"Praveena","sequence":"first","affiliation":[{"name":"Dr.N.G.P. Institute of Technology  Coimbatore Tamil Nadu India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-0570-4723","authenticated-orcid":false,"given":"S.","family":"Anbumani","sequence":"additional","affiliation":[{"name":"Research Scholar, Dr.N.G.P. Institute of Technology Anna University  Coimbatore Tamil Nadu India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"M.","family":"Nirmala","sequence":"additional","affiliation":[{"name":"Dr.N.G.P. Institute of Technology  Coimbatore Tamil Nadu India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"S.","family":"Karthik","sequence":"additional","affiliation":[{"name":"SNS College of Technology  Coimbatore Tamil Nadu India"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2025,8,30]]},"reference":[{"key":"e_1_2_11_2_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11517-021-02474-z"},{"key":"e_1_2_11_3_1","doi-asserted-by":"publisher","DOI":"10.1049\/iet-ipr.2019.1104"},{"key":"e_1_2_11_4_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11760\u2010024\u201003084\u20106"},{"key":"e_1_2_11_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2023.105930"},{"key":"e_1_2_11_6_1","doi-asserted-by":"publisher","DOI":"10.1002\/ima.23041"},{"key":"e_1_2_11_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2024.108601"},{"key":"e_1_2_11_8_1","doi-asserted-by":"publisher","DOI":"10.3390\/info14070389"},{"key":"e_1_2_11_9_1","unstructured":"Deepika T. andG. K.Kannan.2020.\u201cA Novel Adaptive Optimization of Dual\u2010Tree Complex Wavelet Transform for Medical Image Fusion.\u201darXiv preprint arXiv: 2007.13538.https:\/\/doi.org\/10.48550\/arXiv.2007.13538."},{"key":"e_1_2_11_10_1","doi-asserted-by":"crossref","unstructured":"Gong Z. B.Peng A.Shen et\u00a0al.2022.\u201cAttention\u2010Based Densely Connected Convolutional Network for Chromosome Classification.\u201dIn Proceedings of the 8th International Conference on Computing and Artificial Intelligence (pp. 534\u2013540).","DOI":"10.1145\/3532213.3532294"},{"key":"e_1_2_11_11_1","doi-asserted-by":"publisher","DOI":"10.1155\/2020\/8279342"},{"key":"e_1_2_11_12_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11042\u2010024\u201020242\u20107"},{"key":"e_1_2_11_13_1","doi-asserted-by":"crossref","unstructured":"Jung H. B.Park S.Lee et\u00a0al.2020.\u201cDeep Metric Learning Network Using Proxies for Chromosome Classification and Retrieval in Karyotyping Test.\u201dbioRxiv 2020\u201305.https:\/\/doi.org\/10.1101\/2020.05.24.113936.","DOI":"10.1101\/2020.05.24.113936"},{"key":"e_1_2_11_14_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2023.105891"},{"key":"e_1_2_11_15_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11517\u2010021\u201002384\u20100"},{"key":"e_1_2_11_16_1","doi-asserted-by":"crossref","unstructured":"Kuang H. Y.Liang N.Liu J.Liu andJ.Wang.2021.\u201cBEA\u2010SegNet: Body and Edge Aware Network for Medical Image Segmentation.\u201dIn 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (pp. 939\u2010944). IEEE.https:\/\/doi.org\/10.1109\/BIBM52615.2021.9669545.","DOI":"10.1109\/BIBM52615.2021.9669545"},{"key":"e_1_2_11_17_1","doi-asserted-by":"publisher","DOI":"10.1080\/21655979.2020.1778913"},{"key":"e_1_2_11_18_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiolchem.2022.107731"},{"key":"e_1_2_11_19_1","doi-asserted-by":"publisher","DOI":"10.1109\/TCBB.2020.3003445"},{"key":"e_1_2_11_20_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598\u2010022\u201011852\u2010y"},{"key":"e_1_2_11_21_1","doi-asserted-by":"publisher","DOI":"10.1109\/TCBBIO.2024.3525409"},{"key":"e_1_2_11_22_1","doi-asserted-by":"crossref","unstructured":"Liu L. X.Fan C.Zhang J.Dai Y.Xie andX.Liang.2023.\u201cThree\u2010Dimensional Medical Image Fusion With Deformable Cross\u2010Attention.\u201dIn International Conference on Neural Information Processing (551\u2010563). Singapore: Springer Nature Singapore.https:\/\/doi.org\/10.48550\/arXiv.2310.06291.","DOI":"10.1007\/978-981-99-8141-0_41"},{"key":"e_1_2_11_23_1","doi-asserted-by":"publisher","DOI":"10.1049\/syb2.12042"},{"key":"e_1_2_11_24_1","doi-asserted-by":"crossref","unstructured":"Meenakshisundaram N. andG.Ramkumar.2022.\u201cA Combined Deep CNN\u2010LSTM Network for Chromosome Classification for Metaphase Selection.\u201dIn 2022 International Conference on Inventive Computation Technologies (ICICT) (pp. 1005\u20131010). IEEE.","DOI":"10.1109\/ICICT54344.2022.9850651"},{"key":"e_1_2_11_25_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11045\u2010022\u201000819\u2010x"},{"key":"e_1_2_11_26_1","doi-asserted-by":"crossref","unstructured":"Nguyen Q. A. N. T.Nguyen S. H.Nguyen et\u00a0al.2023.\u201cImproving Classification of Curved Chromosomes in Karyotyping Using CNN\u2010Based Deformation.\u201dIn 2023 IEEE Statistical Signal Processing Workshop (SSP) (pp. 285\u2013289). IEEE.https:\/\/doi.org\/10.1109\/SSP53291.2023.10208061.","DOI":"10.1109\/SSP53291.2023.10208061"},{"key":"e_1_2_11_27_1","doi-asserted-by":"crossref","unstructured":"Pal B. S.Mahajan andS.Jain.2020.\u201cMedical Image Fusion Employing Enhancement Techniques.\u201dIn 2020 IEEE International Women in Engineering (WIE) Conference on Electrical and Computer Engineering (WIECON\u2010ECE) (pp. 223\u2010226). IEEE.https:\/\/doi.org\/10.1109\/WIECON\u2010ECE52138.2020.9398021.","DOI":"10.1109\/WIECON-ECE52138.2020.9398021"},{"key":"e_1_2_11_28_1","doi-asserted-by":"crossref","unstructured":"Pijackova K. T.Gotthans andJ.Gotthans.2022.\u201cDeep Learning Pipeline for Chromosome Segmentation.\u201dIn 2022 32nd International Conference Radioelektronika (RADIOELEKTRONIKA) (pp. 01\u201005). IEEE.https:\/\/doi.org\/10.1109\/RADIOELEKTRONIKA54537.2022.9764950.","DOI":"10.1109\/RADIOELEKTRONIKA54537.2022.9764950"},{"key":"e_1_2_11_29_1","doi-asserted-by":"crossref","unstructured":"Remya R. S. 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