The joumal uses Crossref ited-by service counts times cited of published aricles. Citedby allows Crossref members to find out who is citing their conten. This is the Citing Article List of “Comparative analysis of transformer architectures for brain tumor classification”.
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No. |
Publication Date |
Citing Article |
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1 |
2026 |
Mubashar Tariq, Kiho Choi. Swin–YOLOv12: A Hybrid Transformer-Based Deep Learning Approach for Enhanced Real-Time Brain Tumor Detection in MRI Images, Mathematics. 2026; 14: 1447. https://doi.org/10.3390/math14091447 |
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2 |
2026 |
Madallah Alruwaili, Mahmood A. Mahmood. A Dual-Branch Frequency-Aware Attention Framework for Rare Neurological Disease Classification from Brain MRI, Diagnostics. 2026; 16: 1749. https://doi.org/10.3390/diagnostics16111749 |
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3 |
2026 |
Abhishek Jha, Anish Sutradhar, Arnav K, Bratin Das, Mahesh T. R. 2026; 1. https://doi.org/10.1109/CONIT69683.2026.11620714 |
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4 |
2026 |
Md Saiful Islam, André Chéagé Chamgoué, Gurvinder Pal Dubb. Benchmarking State-of-the-Art Vision Transformer Architectures for the Automated Classification of Pigmented Skin Lesions, Computers and Electronics in Medicine. 2026; 3: 42. https://doi.org/10.69882/adba.cem.2026015 |
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5 |
2026 |
Javanshir Zeynalov, Yigitcan Cakmak, Ishak Pacal, Maftun Aliyev. Deep Learning for Tomato Leaf Disease Classification: Comparative Benchmarking of CNN and Vision Transformer Architectures, Trends in Computer Science and Information Technology. 2026; 027. https://doi.org/10.17352/tcsit.000106 |
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6 |
2026 |
Suleyman Naral, Yigitcan Cakmak, Ishak Pacal. Comparative evaluation of vision transformers and convolutional networks for breast ultrasound image classification, Exploration of Medicine. 2026; 7: 1001382. https://doi.org/10.37349/emed.2026.1001382 |
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7 |
2026 |
Elif İlgazi Kılıç, Şafak Kılıç, Wencheng Zhu. Multi-scale feature integration with enhanced cytomorph for high-accuracy cervical cytology classification, PLOS One. 2026; 21: e0351063. https://doi.org/10.1371/journal.pone.0351063 |
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8 |
2026 |
Seyed Amirali Zakavi, Parastoo Radnia, Mahdi Abdollahi Karizno, Armin Tafazolimoghadam, Hamidreza Amiri, Niloofar Deravi, Mobina Fathi. Comparative evaluation of convolutional neural networks and Swin transformer for multiclass brain tumor MRI classification, Discover Applied Sciences. 2026; 8: 910. https://doi.org/10.1007/s42452-026-08767-y |
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9 |
2026 |
Umesh Kumar Lilhore, Vanusha D., Srilatha Gundapaneni, Anto Lourdu Xavier Raj Arockia Selvarathinam, Rasmi A., Sarita Simaiya, Lidia Gosy Tekeste, Ehab Seif Ghith, Heba G. Mohamed, Raul Alcaraz Martinez. A Hybrid Deep Learning Framework for Early Detection of Ovarian Cancer Using Ultrasound and MRI Images on a Secure Cloud Platform, Complexity. 2026; 2026: 9924424. https://doi.org/10.1155/cplx/9924424 |
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10 |
2026 |
Büşra Karaağaç Eskibağlar, Yelda Polat Yavuz, Gizem Karagöz Doğan, Ali Algarni, Yigitcan Cakmak, Ishak Pacal. Denta-HybridoNet: a hybrid CNN-transformer architecture for automated detection of developmental dental anomalies in pediatric panoramic radiographs, Biomedical Signal Processing and Control. 2026; 118: 109784109784. https://doi.org/10.1016/j.bspc.2026.109784 |
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11 |
2026 |
Abdul Wahid, Hikmat Ullah Khan, Anam Naz, Fawaz Khaled Alarfaj, Abel C. H. Chen. Hybrid lightweight vision transformers with attention mechanism for feature extraction and classification of product designs, PLOS One. 2026; 21: e0343510. https://doi.org/10.1371/journal.pone.0343510 |
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12 |
2026 |
Elif İlgazi Kılıç, Şafak Kılıç. DeepInsight-Net: a CBAM-enhanced ResNet50 framework with focal loss for robust cervical cancer classification on multi-center datasets, Frontiers in Medicine. 2026; 13: 1783634. https://doi.org/10.3389/fmed.2026.1783634 |
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13 |
2026 |
Furkan Sönmez, Fevzi Das. Bridging the Gap Between Theoretical Performance and Clinical Utility in Multi-Class Skin Lesion Diagnosis, Artificial Intelligence in Applied Sciences. 2026; 2: 32. https://doi.org/10.69882/adba.ai.2026015 |
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14 |
2026 |
Sifeu Takougang Kingni, Said Baawain. Comparative Evaluation of Convolutional Neural Network Architectures for Automated Skin Cancer Classification: A Study on the ISIC 2018 Dataset, Artificial Intelligence in Applied Sciences. 2026; 2: 8. https://doi.org/10.69882/adba.ai.2026012 |
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15 |
2026 |
R. Radha, Aravindhan Kurunthachalam. 2026; 1968. https://doi.org/10.1109/ICCNCT68477.2026.11589963 |
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16 |
2026 |
Ahmad Musa Arghistani, Hasan Erbay, Ahmet Özdil. A multi-scale attention and efficient convolution framework for brain tumor classification in medical imaging analytics, Healthcare Analytics. 2026; 10: 100481100481. https://doi.org/10.1016/j.health.2026.100481 |