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Original Article
Multi-Cancer Detection Using CNN, Inceptionv3, and Vision Transformer (Vit)
Saleha Habeeba1
Dr. Khaja Mahabubullah2
1 Student, MCA, Deccan College of Engineering and Technology, Hyderabad, Telangana, India. 2 Professor & HOD, MCA, Deccan College of Engineering and Technology, Hyderabad, Telangana, India.
Published Online: July-August 2025
Pages: 39-43
Cite this article
↗ https://www.doi.org/10.59256/ijire.20250604006
References
1. A. Dosovitskiy et al., “An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale,” arXiv preprint arXiv: 2010.11929, 2020.
2. C. Szegedy et al., “Rethinking the Inception Architecture for Computer Vision,” in Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 2818–2826.
3. A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet Classification with Deep Convolutional Neural Networks,” in Advances in Neural Information Processing Systems, vol. 25, pp. 1097–1105, 2012.
4. F. Chollet, “Xception: Deep Learning with Depthwise Separable Convolutions,” in Proc. IEEE CVPR, 2017, pp. 1251–1258.
5. M. Abadi et al., “TensorFlow: A System for Large-Scale Machine Learning,” in Proc. USENIX OSDI, 2016, pp. 265–283.
6. F. Chollet, “Keras,” [Online]. Available: https://keras.io, 2015. [Accessed: 08-Jun-2025].
7. Streamlit Inc., “Streamlit — Turn Data Scripts into Shareable Web Apps in Minutes,” [Online]. Available: https://streamlit.io. [Accessed: 08-Jun-2025].
8. J. Deng et al., “ImageNet: A Large-Scale Hierarchical Image Database,” in Proc. IEEE CVPR, 2009, pp. 248–255.
9. K. Simonyan and A. Zisserman, “Very Deep Convolutional Networks for Large-Scale Image Recognition,” arXiv preprint arXiv: 1409.1556, 2014.
10. T. Lin et al., “Microsoft COCO: Common Objects in Context,” in Proc. ECCV, 2014, pp. 740–755.
11. B. Rajpurkar et al., “CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning,” arXiv preprint arXiv: 1711.05225, 2017.
12. H. Chen et al., “Multimodal Co-Attention Neural Network for Image and Text Matching,” in Proc. IEEE ICCV, 2017, pp. 4223–4231.
13. S. Mehta et al., “ESPNetv2: A Light-weight, Power Efficient, and General Purpose Convolutional Neural Network,” in Proc. CVPR, 2019, pp. 9190–9200.
14. M. Esteva et al., “Dermatologist-level Classification of Skin Cancer with Deep Neural Networks,” Nature, vol. 542, no. 7639, pp. 115–118, 2017.
15. J. Long, E. Shelhamer, and T. Darrell, “Fully Convolutional Networks for Semantic Segmentation,” in Proc. IEEE CVPR, 2015, pp. 3431–3440.
16. G. Litjens et al., “A Survey on Deep Learning in Medical Image Analysis,” Medical Image Analysis, vol. 42, pp. 60–88, 2017.
17. S. Minaee et al., “Image Segmentation Using Deep Learning: A Survey,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 44, no. 7, pp. 3523–3542, 2022.
18. T.-Y. Lin et al., “Focal Loss for Dense Object Detection,” in Proc. ICCV, 2017, pp. 2980–2988.
19. D. Shen, G. Wu, and H.-I. Suk, “Deep Learning in Medical Image Analysis,” Annual Review of Biomedical Engineering, vol. 19, pp. 221–248, 2017.
20. W. Bai et al., “Self-Supervised Learning for Cardiac MR Image Segmentation by Anatomical Position Prediction,” in Proc. MICCAI, 2019, pp. 541–549.
2. C. Szegedy et al., “Rethinking the Inception Architecture for Computer Vision,” in Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 2818–2826.
3. A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet Classification with Deep Convolutional Neural Networks,” in Advances in Neural Information Processing Systems, vol. 25, pp. 1097–1105, 2012.
4. F. Chollet, “Xception: Deep Learning with Depthwise Separable Convolutions,” in Proc. IEEE CVPR, 2017, pp. 1251–1258.
5. M. Abadi et al., “TensorFlow: A System for Large-Scale Machine Learning,” in Proc. USENIX OSDI, 2016, pp. 265–283.
6. F. Chollet, “Keras,” [Online]. Available: https://keras.io, 2015. [Accessed: 08-Jun-2025].
7. Streamlit Inc., “Streamlit — Turn Data Scripts into Shareable Web Apps in Minutes,” [Online]. Available: https://streamlit.io. [Accessed: 08-Jun-2025].
8. J. Deng et al., “ImageNet: A Large-Scale Hierarchical Image Database,” in Proc. IEEE CVPR, 2009, pp. 248–255.
9. K. Simonyan and A. Zisserman, “Very Deep Convolutional Networks for Large-Scale Image Recognition,” arXiv preprint arXiv: 1409.1556, 2014.
10. T. Lin et al., “Microsoft COCO: Common Objects in Context,” in Proc. ECCV, 2014, pp. 740–755.
11. B. Rajpurkar et al., “CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning,” arXiv preprint arXiv: 1711.05225, 2017.
12. H. Chen et al., “Multimodal Co-Attention Neural Network for Image and Text Matching,” in Proc. IEEE ICCV, 2017, pp. 4223–4231.
13. S. Mehta et al., “ESPNetv2: A Light-weight, Power Efficient, and General Purpose Convolutional Neural Network,” in Proc. CVPR, 2019, pp. 9190–9200.
14. M. Esteva et al., “Dermatologist-level Classification of Skin Cancer with Deep Neural Networks,” Nature, vol. 542, no. 7639, pp. 115–118, 2017.
15. J. Long, E. Shelhamer, and T. Darrell, “Fully Convolutional Networks for Semantic Segmentation,” in Proc. IEEE CVPR, 2015, pp. 3431–3440.
16. G. Litjens et al., “A Survey on Deep Learning in Medical Image Analysis,” Medical Image Analysis, vol. 42, pp. 60–88, 2017.
17. S. Minaee et al., “Image Segmentation Using Deep Learning: A Survey,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 44, no. 7, pp. 3523–3542, 2022.
18. T.-Y. Lin et al., “Focal Loss for Dense Object Detection,” in Proc. ICCV, 2017, pp. 2980–2988.
19. D. Shen, G. Wu, and H.-I. Suk, “Deep Learning in Medical Image Analysis,” Annual Review of Biomedical Engineering, vol. 19, pp. 221–248, 2017.
20. W. Bai et al., “Self-Supervised Learning for Cardiac MR Image Segmentation by Anatomical Position Prediction,” in Proc. MICCAI, 2019, pp. 541–549.
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