*Result*: Chromosome Image Classification Using Edge Fusion Attention Network.
Title:
Chromosome Image Classification Using Edge Fusion Attention Network.
Authors:
Praveena V; Dr.N.G.P. Institute of Technology, Coimbatore, Tamil Nadu, India., Anbumani S; Research Scholar, Dr.N.G.P. Institute of Technology, Anna University, Coimbatore, Tamil Nadu, India., Nirmala M; Dr.N.G.P. Institute of Technology, Coimbatore, Tamil Nadu, India., Karthik S; SNS College of Technology, Coimbatore, Tamil Nadu, India.
Source:
Microscopy research and technique [Microsc Res Tech] 2026 Feb; Vol. 89 (2), pp. 230-242. Date of Electronic Publication: 2025 Aug 30.
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: Wiley-Liss Country of Publication: United States NLM ID: 9203012 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1097-0029 (Electronic) Linking ISSN: 1059910X NLM ISO Abbreviation: Microsc Res Tech Subsets: MEDLINE
Imprint Name(s):
Original Publication: New York, NY : Wiley-Liss, c1992-
MeSH Terms:
References:
Albayrak, A. 2022. “Classification of Analyzable Metaphase Images Using Transfer Learning and Fine Tuning.” Medical & Biological Engineering & Computing 60: 239–248.
Altinsoy, E., J. Yang, and C. Yilmaz. 2020. “Fully‐Automatic Raw G‐Band Chromosome Image Segmentation.” IET Image Processing 14, no. 9: 1920–1928.
Calik, N. 2024. “The Model Architecture Search System for Chromosome Image Classification.” Signal, Image and Video Processing 18, no. 5: 4435–4445. https://doi.org/10.1007/s11760‐024‐03084‐6.
Chang, L., K. Wu, C. Gu, and C. Chen. 2024. “A Novel End‐To‐End Chromosome Classification Approach Using Deep Neural Network With Triple Attention Mechanism.” Biomedical Signal Processing and Control 91: 105930.
Chavan, S., L. Nair, N. Nimbalkar, and S. Solkar. 2024. “Karyotyping of Human Chromosomes in Metaphase Images Using Faster R‐CNN and Inception Models.” International Journal of Imaging Systems and Technology 34, no. 2: e23041. https://doi.org/10.1002/ima.23041.
Chen, S., K. Zhang, J. Hu, et al. 2024. “KaryoXpert: An Accurate Chromosome Segmentation and Classification Framework for Karyotyping Analysis Without Training With Manually Labeled Metaphase‐Image Mask Annotations.” Computers in Biology and Medicine 177: 108601. https://doi.org/10.1016/j.compbiomed.2024.108601.
D'Angelo, M., and L. Nanni. 2023. “Deep‐Learning‐Based Human Chromosome Classification: Data Augmentation and Ensemble.” Information (Basel) 14, no. 7: 389. https://doi.org/10.3390/info14070389.
Deepika, T., and G. K. Kannan. 2020. “A Novel Adaptive Optimization of Dual‐Tree Complex Wavelet Transform for Medical Image Fusion.” arXiv preprint arXiv: 2007.13538. https://doi.org/10.48550/arXiv.2007.13538.
Gong, Z., B. Peng, A. Shen, et al. 2022. “Attention‐Based Densely Connected Convolutional Network for Chromosome Classification.” In Proceedings of the 8th International Conference on Computing and Artificial Intelligence (pp. 534–540).
Huang, B., F. Yang, M. Yin, X. Mo, and C. Zhong. 2020. “A Review of Multimodal Medical Image Fusion Techniques.” Computational and Mathematical Methods in Medicine 2020, no. 1: 8279342. https://doi.org/10.1155/2020/8279342.
Isfahani, F. P., H. Pourghassem, H. Mahdavi‐Nasab, and A. Naghsh. 2024. “Chromosome Analysis Using a Hybrid Deep CNN and Structural Feature‐Based Grouping Model.” Multimedia Tools and Applications 84: 1–30. https://doi.org/10.1007/s11042‐024‐20242‐7.
Jung, H., B. Park, S. Lee, et al. 2020. “Deep Metric Learning Network Using Proxies for Chromosome Classification and Retrieval in Karyotyping Test.” bioRxiv, 2020–05. https://doi.org/10.1101/2020.05.24.113936.
Kang, S., J. Han, I. Lee, H. Joo, Y. Chung, and S. Yang. 2024. “Chromosome Analysis Method Based on Deep Learning: Counting Chromosomes and Detecting Abnormal Chromosomes.” Biomedical Signal Processing and Control 91: 105891.
Kanimozhi, V. S., M. Balasubramani, and R. Anuradha. 2021. “Hierarchal Bayes Model With AlexNet for Characterization of M‐FISH Chromosome Images.” Medical and Biological Engineering and Computing 59, no. 7: 1529–1544. https://doi.org/10.1007/s11517‐021‐02384‐0.
Kuang, H., Y. Liang, N. Liu, J. Liu, and J. Wang. 2021. “BEA‐SegNet: Body and Edge Aware Network for Medical Image Segmentation.” In 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (pp. 939‐944). IEEE. https://doi.org/10.1109/BIBM52615.2021.9669545.
Li, X., H. Jiao, and Y. Wang. 2020. “Edge Detection Algorithm of Cancer Image Based on Deep Learning.” Bioengineered 11, no. 1: 693–707. https://doi.org/10.1080/21655979.2020.1778913.
Lin, C., H. Chen, J. Huang, et al. 2022. “ChromosomeNet: A Massive Dataset Enabling Benchmarking and Building Basedlines of Clinical Chromosome Classification.” Computational Biology and Chemistry 100: 107731.
Lin, C., G. Zhao, Z. Yang, et al. 2020. “Cir‐Net: Automatic Classification of Human Chromosome Based on Inception‐Resnet Architecture.” IEEE/ACM Transactions on Computational Biology and Bioinformatics 19, no. 3: 1285–1293. https://doi.org/10.1109/TCBB.2020.3003445.
Liu, H., Y. Feng, H. Xu, et al. 2022. “MEA‐Net: Multilayer Edge Attention Network for Medical Image Segmentation.” Scientific Reports 12, no. 1: 7868. https://doi.org/10.1038/s41598‐022‐11852‐y.
Liu, H., X. Li, X. Fan, Z. Lu, and L. Zhang. 2025. “ESNet: End‐To‐End Chromosome Instance Segmentation Method Based on Edge Supervised Network.” IEEE Transactions on Computational Biology and Bioinformatics 22: 368–374. https://doi.org/10.1109/TCBBIO.2024.3525409.
Liu, L., X. Fan, C. Zhang, J. Dai, Y. Xie, and X. Liang. 2023. “Three‐Dimensional Medical Image Fusion With Deformable Cross‐Attention.” In International Conference on Neural Information Processing (551‐563). Singapore: Springer Nature Singapore. https://doi.org/10.48550/arXiv.2310.06291.
Liu, X., L. Fu, J. Chun‐Wei Lin, and S. Liu. 2022. “SRAS‐Net: Low‐Resolution Chromosome Image Classification Based on Deep Learning.” IET Systems Biology 16, no. 3–4: 85–97. https://doi.org/10.1049/syb2.12042.
Meenakshisundaram, N., and G. Ramkumar. 2022. “A Combined Deep CNN‐LSTM Network for Chromosome Classification for Metaphase Selection.” In 2022 International Conference on Inventive Computation Technologies (ICICT) (pp. 1005–1010). IEEE.
Menaka, D., and S. G. Vaidyanathan. 2022. “Chromenet: A CNN Architecture With Comparison of Optimizers for Classification of Human Chromosome Images.” Multidimensional Systems and Signal Processing 33, no. 3: 747–768. https://doi.org/10.1007/s11045‐022‐00819‐x.
Nguyen, Q. A., N. T. Nguyen, S. H. Nguyen, et al. 2023. “Improving Classification of Curved Chromosomes in Karyotyping Using CNN‐Based Deformation.” In 2023 IEEE Statistical Signal Processing Workshop (SSP) (pp. 285–289). IEEE. https://doi.org/10.1109/SSP53291.2023.10208061.
Pal, B., S. Mahajan, and S. Jain. 2020. “Medical Image Fusion Employing Enhancement Techniques.” In 2020 IEEE International Women in Engineering (WIE) Conference on Electrical and Computer Engineering (WIECON‐ECE) (pp. 223‐226). IEEE. https://doi.org/10.1109/WIECON‐ECE52138.2020.9398021.
Pijackova, K., T. Gotthans, and J. Gotthans. 2022. “Deep Learning Pipeline for Chromosome Segmentation.” In 2022 32nd International Conference Radioelektronika (RADIOELEKTRONIKA) (pp. 01‐05). IEEE. https://doi.org/10.1109/RADIOELEKTRONIKA54537.2022.9764950.
Remya, R. S., S. Hariharan, M. Sooraj, V. Keerthi, A. S. Raj, and C. Gopakumar. 2020. “Deepnet for Detecting Analyzable Metaphases.” In 2020 Advanced Computing and Communication Technologies for High Performance Applications (ACCTHPA) (pp. 1‐7). IEEE. https://doi.org/10.1109/ACCTHPA49271.2020.9213212.
Saranya, S., and S. Lakshmi. 2023. “Classification of Chromosomes to Diagnose Chromosomal Abnormalities Using CNN.” In 2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF) (pp. 1–5). IEEE.
Sekar, S., and L. Sankaran. 2024. “Enhanced Karyotyping Through Deep Learning‐Assisted Segmentation and Classification of Chromosomal Cells.” Traitement du Signal 41, no. 1: 477–484.
Somasundaram, D., A. Chapparadahallimath, M. Krishanan, and N. Madian. 2023. “A Novel Automated Chromosome Analyzer Software Bundle for Karyotyping and Birth Defect Analysis.” IEEE Access 11: 145516–145526. https://doi.org/10.1109/ACCESS.2023.3344664.
Thinh, N. H., N. H. H. Son, P. T. V. Huong, et al. 2020. “A Web‐Based Tool for Semi‐Interactively Karyotyping the Chromosome Images for Analyzing Chromosome Abnormalities.” National Foundation for Science and Technology Development Conference on Information and Computer Science. https://doi.org/10.1109/NICS51282.2020.9335893.
Tirupal, T., B. C. Mohan, and S. S. Kumar. 2021. “Multimodal Medical Image Fusion Techniques–a Review.” Current Signal Transduction Therapy 16, no. 2: 142–163. https://doi.org/10.2174/1574362415666200226103116.
Vajen, B., S. Hänselmann, F. Lutterloh, et al. 2022. “Classification of Fluorescent R‐Band Metaphase Chromosomes Using a Convolutional Neural Network Is Precise and Fast in Generating Karyograms of Hematologic Neoplastic Cells.” Cancer Genetics 260: 23–29.
Wang, C., L. Yu, J. Su, et al. 2024. “Fully Automatic Karyotyping via Deep Convolutional Neural Networks.” IEEE Access 12: 46081–46092. https://doi.org/10.1109/ACCESS.2024.3380829.
Wei, H., W. Gao, H. Nie, J. Sun, and M. Zhu. 2022. “Classification of Giemsa Staining Chromosome Using Input‐Aware Deep Convolutional Neural Network With Integrated Uncertainty Estimates.” Biomedical Signal Processing and Control 71: 103120.
Wu, Q., P. Suetens, and A. Oosterlinck. 1990. “Chromosome Classification Using a Multi‐Layer Perceptron Neural Net.” Proc. 12th Annual Int. Conf. IEEE Engineering in Medicine and Biology Society vol 12. pp. 1453–4.
Xiao, L., and C. Luo. 2021. “DEEPACC: Automate Chromosome Classification Based on Metaphase Images Using Deep Learning Framework Fused With Priori Knowledge.” In 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI). (607–610). IEEE.
Zhang, J., W. Hu, S. Li, et al. 2021. “Chromosome Classification and Straightening Based on an Interleaved and Multi‐Task Network.” IEEE Journal of Biomedical and Health Informatics 25, no. 8: 3240–3251. https://doi.org/10.1109/JBHI.2021.3062234.
Zhang, L., X. Fan, K. Lin, R. Qi, X. Yi, and H. Liu. 2024. “ChromEDA: Chromosome Classification by Ensemble Framework‐Based Domain Adaptation.” Microscopy Research and Technique 87, no. 4: 832–843. https://doi.org/10.1002/jemt.24461.
Zhao, Y., G. Song, and P. Wang. 2024. “Enhanced Label Constrained Contrastive Learning for Chromosome Optical Microscopic Image Classification.” Biomedical Signal Processing and Control 90: 105825. https://doi.org/10.1016/j.bspc.2023.105825.
Altinsoy, E., J. Yang, and C. Yilmaz. 2020. “Fully‐Automatic Raw G‐Band Chromosome Image Segmentation.” IET Image Processing 14, no. 9: 1920–1928.
Calik, N. 2024. “The Model Architecture Search System for Chromosome Image Classification.” Signal, Image and Video Processing 18, no. 5: 4435–4445. https://doi.org/10.1007/s11760‐024‐03084‐6.
Chang, L., K. Wu, C. Gu, and C. Chen. 2024. “A Novel End‐To‐End Chromosome Classification Approach Using Deep Neural Network With Triple Attention Mechanism.” Biomedical Signal Processing and Control 91: 105930.
Chavan, S., L. Nair, N. Nimbalkar, and S. Solkar. 2024. “Karyotyping of Human Chromosomes in Metaphase Images Using Faster R‐CNN and Inception Models.” International Journal of Imaging Systems and Technology 34, no. 2: e23041. https://doi.org/10.1002/ima.23041.
Chen, S., K. Zhang, J. Hu, et al. 2024. “KaryoXpert: An Accurate Chromosome Segmentation and Classification Framework for Karyotyping Analysis Without Training With Manually Labeled Metaphase‐Image Mask Annotations.” Computers in Biology and Medicine 177: 108601. https://doi.org/10.1016/j.compbiomed.2024.108601.
D'Angelo, M., and L. Nanni. 2023. “Deep‐Learning‐Based Human Chromosome Classification: Data Augmentation and Ensemble.” Information (Basel) 14, no. 7: 389. https://doi.org/10.3390/info14070389.
Deepika, T., and G. K. Kannan. 2020. “A Novel Adaptive Optimization of Dual‐Tree Complex Wavelet Transform for Medical Image Fusion.” arXiv preprint arXiv: 2007.13538. https://doi.org/10.48550/arXiv.2007.13538.
Gong, Z., B. Peng, A. Shen, et al. 2022. “Attention‐Based Densely Connected Convolutional Network for Chromosome Classification.” In Proceedings of the 8th International Conference on Computing and Artificial Intelligence (pp. 534–540).
Huang, B., F. Yang, M. Yin, X. Mo, and C. Zhong. 2020. “A Review of Multimodal Medical Image Fusion Techniques.” Computational and Mathematical Methods in Medicine 2020, no. 1: 8279342. https://doi.org/10.1155/2020/8279342.
Isfahani, F. P., H. Pourghassem, H. Mahdavi‐Nasab, and A. Naghsh. 2024. “Chromosome Analysis Using a Hybrid Deep CNN and Structural Feature‐Based Grouping Model.” Multimedia Tools and Applications 84: 1–30. https://doi.org/10.1007/s11042‐024‐20242‐7.
Jung, H., B. Park, S. Lee, et al. 2020. “Deep Metric Learning Network Using Proxies for Chromosome Classification and Retrieval in Karyotyping Test.” bioRxiv, 2020–05. https://doi.org/10.1101/2020.05.24.113936.
Kang, S., J. Han, I. Lee, H. Joo, Y. Chung, and S. Yang. 2024. “Chromosome Analysis Method Based on Deep Learning: Counting Chromosomes and Detecting Abnormal Chromosomes.” Biomedical Signal Processing and Control 91: 105891.
Kanimozhi, V. S., M. Balasubramani, and R. Anuradha. 2021. “Hierarchal Bayes Model With AlexNet for Characterization of M‐FISH Chromosome Images.” Medical and Biological Engineering and Computing 59, no. 7: 1529–1544. https://doi.org/10.1007/s11517‐021‐02384‐0.
Kuang, H., Y. Liang, N. Liu, J. Liu, and J. Wang. 2021. “BEA‐SegNet: Body and Edge Aware Network for Medical Image Segmentation.” In 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (pp. 939‐944). IEEE. https://doi.org/10.1109/BIBM52615.2021.9669545.
Li, X., H. Jiao, and Y. Wang. 2020. “Edge Detection Algorithm of Cancer Image Based on Deep Learning.” Bioengineered 11, no. 1: 693–707. https://doi.org/10.1080/21655979.2020.1778913.
Lin, C., H. Chen, J. Huang, et al. 2022. “ChromosomeNet: A Massive Dataset Enabling Benchmarking and Building Basedlines of Clinical Chromosome Classification.” Computational Biology and Chemistry 100: 107731.
Lin, C., G. Zhao, Z. Yang, et al. 2020. “Cir‐Net: Automatic Classification of Human Chromosome Based on Inception‐Resnet Architecture.” IEEE/ACM Transactions on Computational Biology and Bioinformatics 19, no. 3: 1285–1293. https://doi.org/10.1109/TCBB.2020.3003445.
Liu, H., Y. Feng, H. Xu, et al. 2022. “MEA‐Net: Multilayer Edge Attention Network for Medical Image Segmentation.” Scientific Reports 12, no. 1: 7868. https://doi.org/10.1038/s41598‐022‐11852‐y.
Liu, H., X. Li, X. Fan, Z. Lu, and L. Zhang. 2025. “ESNet: End‐To‐End Chromosome Instance Segmentation Method Based on Edge Supervised Network.” IEEE Transactions on Computational Biology and Bioinformatics 22: 368–374. https://doi.org/10.1109/TCBBIO.2024.3525409.
Liu, L., X. Fan, C. Zhang, J. Dai, Y. Xie, and X. Liang. 2023. “Three‐Dimensional Medical Image Fusion With Deformable Cross‐Attention.” In International Conference on Neural Information Processing (551‐563). Singapore: Springer Nature Singapore. https://doi.org/10.48550/arXiv.2310.06291.
Liu, X., L. Fu, J. Chun‐Wei Lin, and S. Liu. 2022. “SRAS‐Net: Low‐Resolution Chromosome Image Classification Based on Deep Learning.” IET Systems Biology 16, no. 3–4: 85–97. https://doi.org/10.1049/syb2.12042.
Meenakshisundaram, N., and G. Ramkumar. 2022. “A Combined Deep CNN‐LSTM Network for Chromosome Classification for Metaphase Selection.” In 2022 International Conference on Inventive Computation Technologies (ICICT) (pp. 1005–1010). IEEE.
Menaka, D., and S. G. Vaidyanathan. 2022. “Chromenet: A CNN Architecture With Comparison of Optimizers for Classification of Human Chromosome Images.” Multidimensional Systems and Signal Processing 33, no. 3: 747–768. https://doi.org/10.1007/s11045‐022‐00819‐x.
Nguyen, Q. A., N. T. Nguyen, S. H. Nguyen, et al. 2023. “Improving Classification of Curved Chromosomes in Karyotyping Using CNN‐Based Deformation.” In 2023 IEEE Statistical Signal Processing Workshop (SSP) (pp. 285–289). IEEE. https://doi.org/10.1109/SSP53291.2023.10208061.
Pal, B., S. Mahajan, and S. Jain. 2020. “Medical Image Fusion Employing Enhancement Techniques.” In 2020 IEEE International Women in Engineering (WIE) Conference on Electrical and Computer Engineering (WIECON‐ECE) (pp. 223‐226). IEEE. https://doi.org/10.1109/WIECON‐ECE52138.2020.9398021.
Pijackova, K., T. Gotthans, and J. Gotthans. 2022. “Deep Learning Pipeline for Chromosome Segmentation.” In 2022 32nd International Conference Radioelektronika (RADIOELEKTRONIKA) (pp. 01‐05). IEEE. https://doi.org/10.1109/RADIOELEKTRONIKA54537.2022.9764950.
Remya, R. S., S. Hariharan, M. Sooraj, V. Keerthi, A. S. Raj, and C. Gopakumar. 2020. “Deepnet for Detecting Analyzable Metaphases.” In 2020 Advanced Computing and Communication Technologies for High Performance Applications (ACCTHPA) (pp. 1‐7). IEEE. https://doi.org/10.1109/ACCTHPA49271.2020.9213212.
Saranya, S., and S. Lakshmi. 2023. “Classification of Chromosomes to Diagnose Chromosomal Abnormalities Using CNN.” In 2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF) (pp. 1–5). IEEE.
Sekar, S., and L. Sankaran. 2024. “Enhanced Karyotyping Through Deep Learning‐Assisted Segmentation and Classification of Chromosomal Cells.” Traitement du Signal 41, no. 1: 477–484.
Somasundaram, D., A. Chapparadahallimath, M. Krishanan, and N. Madian. 2023. “A Novel Automated Chromosome Analyzer Software Bundle for Karyotyping and Birth Defect Analysis.” IEEE Access 11: 145516–145526. https://doi.org/10.1109/ACCESS.2023.3344664.
Thinh, N. H., N. H. H. Son, P. T. V. Huong, et al. 2020. “A Web‐Based Tool for Semi‐Interactively Karyotyping the Chromosome Images for Analyzing Chromosome Abnormalities.” National Foundation for Science and Technology Development Conference on Information and Computer Science. https://doi.org/10.1109/NICS51282.2020.9335893.
Tirupal, T., B. C. Mohan, and S. S. Kumar. 2021. “Multimodal Medical Image Fusion Techniques–a Review.” Current Signal Transduction Therapy 16, no. 2: 142–163. https://doi.org/10.2174/1574362415666200226103116.
Vajen, B., S. Hänselmann, F. Lutterloh, et al. 2022. “Classification of Fluorescent R‐Band Metaphase Chromosomes Using a Convolutional Neural Network Is Precise and Fast in Generating Karyograms of Hematologic Neoplastic Cells.” Cancer Genetics 260: 23–29.
Wang, C., L. Yu, J. Su, et al. 2024. “Fully Automatic Karyotyping via Deep Convolutional Neural Networks.” IEEE Access 12: 46081–46092. https://doi.org/10.1109/ACCESS.2024.3380829.
Wei, H., W. Gao, H. Nie, J. Sun, and M. Zhu. 2022. “Classification of Giemsa Staining Chromosome Using Input‐Aware Deep Convolutional Neural Network With Integrated Uncertainty Estimates.” Biomedical Signal Processing and Control 71: 103120.
Wu, Q., P. Suetens, and A. Oosterlinck. 1990. “Chromosome Classification Using a Multi‐Layer Perceptron Neural Net.” Proc. 12th Annual Int. Conf. IEEE Engineering in Medicine and Biology Society vol 12. pp. 1453–4.
Xiao, L., and C. Luo. 2021. “DEEPACC: Automate Chromosome Classification Based on Metaphase Images Using Deep Learning Framework Fused With Priori Knowledge.” In 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI). (607–610). IEEE.
Zhang, J., W. Hu, S. Li, et al. 2021. “Chromosome Classification and Straightening Based on an Interleaved and Multi‐Task Network.” IEEE Journal of Biomedical and Health Informatics 25, no. 8: 3240–3251. https://doi.org/10.1109/JBHI.2021.3062234.
Zhang, L., X. Fan, K. Lin, R. Qi, X. Yi, and H. Liu. 2024. “ChromEDA: Chromosome Classification by Ensemble Framework‐Based Domain Adaptation.” Microscopy Research and Technique 87, no. 4: 832–843. https://doi.org/10.1002/jemt.24461.
Zhao, Y., G. Song, and P. Wang. 2024. “Enhanced Label Constrained Contrastive Learning for Chromosome Optical Microscopic Image Classification.” Biomedical Signal Processing and Control 90: 105825. https://doi.org/10.1016/j.bspc.2023.105825.
Contributed Indexing:
Keywords: adaptive edge preserve fusion; chromosome classification; edge fusion attention network; feature extraction; genetic disease diagnosis; karyotyping
Entry Date(s):
Date Created: 20250830 Date Completed: 20260114 Latest Revision: 20260114
Update Code:
20260130
DOI:
10.1002/jemt.70070
PMID:
40884446
Database:
MEDLINE
*Further Information*
*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<sup>2</sup>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<sup>2</sup>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.
(© 2025 Wiley Periodicals LLC.)*