Cancer remains one of the leading causes of mortality worldwide, claiming approximately ten million lives annually. Early and accurate diagnosis is critical for improving patient survival outcomes, yet traditional diagnostic workflows depend heavily on specialized radiologists and pathologists. This paper presents OncoAIFusion, a unified, production-ready artificial intelligence system designed to support multi-cancer diagnosis and prognosis across eight major cancer groups comprising 22 distinct subtypes. The system seamlessly integrates deep convolutional neural networks based on transfer learning, multi-task learning principles, and generative artificial intelligence techniques to analyze medical imaging data across multiple modalities. The core architecture employs ResNet-50 as the backbone with carefully designed task-specific classification heads, automatic image-type detection with intelligent routing, class-imbalance handling through weighted loss functions, and confidence calibration mechanisms. OncoAIFusion incorporates transparency features through clear model confidence reporting and structured diagnostic summaries. The system achieves accuracy exceeding 90% across all supported cancer types with sub-100-millisecond inference latency on standard GPU hardware. Critically, OncoAIFusion is designed as a decision-support tool to augment physician expertise, not to replace clinical judgment. Patient care decisions must remain under physician authority. This work addresses documented barriers to clinical adoption of artificial intelligence tools, including lack of interoperability, insufficient interpretability, deployment complexity, and fragmentation of single-disease tools. OncoAIFusion represents a translational framework bridging the significant gap between academic research prototypes and clinically deployable artificial intelligence systems.

Experimental results demonstrate that the proposed system achieves near-expert-level performance, with several cancer classifiers exceeding 99% accuracy, reinforcing its potential as a reliable clinical decision-support tool when used under physician supervision. Successful clinical translation requires prospective validation, regulatory engagement, and organizational adoption of responsible AI principles. The technical contributions—unified architecture, automatic modality detection, production deployment infrastructure—represent necessary but insufficient conditions for clinical impact. Future work will pursue prospective multicenter validation, independent fairness audits across demographic subgroups, FDA regulatory pathway engagement, and integration partnerships with healthcare systems. Until these steps are completed, OncoAIFusion remains research stage technology. The overarching goal is not to replace radiologists and pathologists, but rather to amplify their diagnostic capacity, standardize recommendations, reduce errors, and ultimately improve patient outcomes through physician-AI collaboration grounded in transparent, ethical principles.

[1]Kumar, Y., Singh, A., Patel, R., and colleagues, “Automating cancer diagnosis using advanced deep learning models across multiple imaging modalities,” Nature Scientific Reports, vol. 14, no. 1, p. 22498, 2024. DOI: 10.1038/s41598-024-75876-2 [2]Chen, L., Wang, X., Zhou, M., Liu, J., and collabo- rators, “Cross-platform multi-cancer histopathology classification using hybrid CNN-Vision Transformer architecture,” Nature Scientific Reports, vol. 15, no. 1, p. 2847, 2025. DOI: 10.1038/s41598-025-24791-1 [3]Wei, S., Johnson, M., Chen, D., Kumar, A., and colleagues, “Deep learning for cancer detection and prognosis based on integrated genomic and imaging data,” PMC National Center for Biotechnology In- formation, vol. 12, no. 4, pp. 459–622, 2025. [4]Krishnapriya, S., Kumar, R., Patel, M., Singh, V., and associates, “Hybrid deep learning models for identifying cancer type and subtype using pre- trained convolutional neural networks,” Computers in Biology and Medicine, vol. 175, p. 108432, 2025. [5]Rodriguez, M., Lee, S., Kim, J., Wang, X., and team, “AI-driven multimodal medical imaging and data fu- sion approaches in precision oncology and health- care,” Frontiers in Medicine, vol. 12, p. 1575753, 2025. [6]Abbasi, M., Rodriguez, C., Thompson, K., Williams, S., and others, “Generative artificial intelligence in healthcare: revolutionizing patient care and clinical diagnosis,” Journal of Healthcare Technology, vol. 18, no. 2, pp. 112–128, 2024. [7]Thompson, R., Smith, J., Lee, K., Davis, M., and associates, “Generative natural language processing systems for automated clinical documentation and diagnostic reporting,” Healthcare AI Review, vol. 5, no. 1, pp. 45–62, 2025. [8]Liu, X., Wang, Y., Chen, Z., Kumar, S., and col- laborators, “From convolutional neural networks to transformers: comprehensive review of medical im- age segmentation models,” IEEE Transactions on Medical Imaging, vol. 43, no. 3, pp. 876–892, 2024. DOI: 10.1109/TMI.2024.3001456 [9]Litjens, G., Kooi, T., Bejnordi, B.E., Setio, A.A.A., Ciompi, F., Ghafoorian, M., van der Laak, J.A.W.M., van Ginneken, B., and S´anchez, C.I., “A survey on deep learning in medical image analysis,” Medical Image Analysis, vol. 42, pp. 60–88, 2019. DOI: 10.1016/j.media.2017.07.005 [10]He, K., Zhang, X., Ren, S., and Sun, J., “Deep resid- ual learning for image recognition,” in Proceedings of IEEE Conference on Computer Vision and Pat- tern Recognition (CVPR), pp. 770–778, 2016. DOI: 10.1109/CVPR.2016.90 [11]Kingma, D.P. and Ba, J., “Adam: A method for stochastic optimization,” in Proceedings of Inter- national Conference on Learning Representations (ICLR), 2014. [12]Ioffe, S. and Szegedy, C., “Batch normalization: ac- celerating deep network training by reducing internal covariate shift,” in Proceedings of International Con- ference on Machine Learning, pp. 448–456, 2015. [13]Hinton, G.E., Srivastava, N., Krizhevsky, A., Sutskever, I., and Salakhutdinov, R.R., “Improv- ing neural networks by preventing co-adaptation of feature detectors,” arXiv preprint arXiv:1207.0580, 2012. [14]Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weis- senborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., and Houlsby, N., “An image is worth 16×16 words: Transformers for image recognition at scale,” in In- ternational Conference on Learning Representations (ICLR), 2021. [15]Yosinski, J., Clune, J., Bengio, Y., and Liphardt, H., “How transferable are features in deep neural net- works?” in Proceedings of Advances in Neural In- formation Processing Systems (NeurIPS), pp. 3320– 3328, 2014. [16]Pan, S.J. and Yang, Q., “A survey on transfer learn- ing,” IEEE Transactions on Knowledge and Data Engineering, vol. 22, no. 10, pp. 1345–1359, 2009. DOI: 10.1109/TKDE.2009.191 [17]Caruana, R., “Multitask learning,” Machine Learn- ing, vol. 28, no. 1, pp. 41–75, 1997. DOI: 10.1023/A:1007379606734 [18]Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., and Polo- sukhin, I., “Attention is all you need,” in Proceed- ings of Advances in Neural Information Processing Systems (NeurIPS), pp. 5998–6008, 2017. [19]Devlin, J., Chang, M.W., Lee, K., and Toutanova, K., “BERT: Pre-training of deep bidirectional trans- formers for language understanding,” arXiv preprint arXiv:1810.04805, 2018. [20]Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., and Antiga, L., “PyTorch: An im- perative style, high-performance deep learning li- brary,” in Proceedings of Advances in Neural In- formation Processing Systems (NeurIPS), pp. 8026– 8037, 2019. [21]Simonyan, K. and Zisserman, A., “Very deep convo- lutional networks for large-scale image recognition,” in International Conference on Learning Represen- tations (ICLR), 2014. [22]Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z., “Rethinking the inception architecture for computer vision,” in Proceedings of IEEE Con- ference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826, 2016. [23]Huang, G., Liu, Z., Van Der Maaten, L., and Wein- berger, K.Q., “Densely connected convolutional net- works,” in Proceedings of IEEE Conference on Com- puter Vision and Pattern Recognition (CVPR), pp. 4700–4708, 2017. [24]Tan, M. and Le, Q.V., “EfficientNet: Rethinking model scaling for convolutional neural networks,” in Proceedings of International Conference on Machine Learning (ICML), pp. 6105–6114, 2019. [25]Ronneberger, O., Fischer, P., and Brox, T., “U- Net: Convolutional networks for biomedical im- age segmentation,” in Medical Image Computing and Computer-Assisted Intervention (MICCAI), pp. 234–241, 2015. [26]Health Level Seven International (HL7), “FHIR (Fast Healthcare Interoperability Resources) Standard,” [Online]. Available: https://www.hl7.org/fhir. [Accessed: Dec. 2025]. [27] Tiangolo, S., “FastAPI: Modern, Fast Web Frame- work for Building APIs with Python,” [Online]. Available: https://fastapi.tiangolo.com. [Accessed: Dec. 2025]. [28]Docker Inc., “Docker: Enterprise Con- tainer Platform,” [Online]. Available: https://www.docker.com. [Accessed: Dec. 2025]. [29]Kaggle, “Brain Tumor Classification Dataset,” [On- line]. Available: https://www.kaggle.com/datasets. [Accessed: Dec. 2025]. [30]Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., and others, “TensorFlow: A system for large-scale machine learning,” in Proceedings of 12th USENIX Symposium on Operating Systems De- sign and Implementation (OSDI), pp. 265–283, 2016.