Early Disease Detection Using AI: A Deep Learning Approach to Predicting Cancer and Neurological Disorders
Downloads
Early diagnosis of life-threatening illnesses, like cancers and neurological disorders, is crucial for enhancing patient survival rates, minimizing treatment expenses, and the administration of early medical intervention. Yet, conventional diagnostic techniques are usually beset by issues that include high reliance on expert opinion, time consumption, and fluctuating accuracy—particularly during the initial phases of disease progression. The availability of huge medical datasets, coupled with the recent explosion in Artificial Intelligence (AI), specifically in the area of deep learning, has opened up a new avenue for strengthening early diagnostic powers.
This study compares and contrasts the performance of five deep learning architectures—Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory networks (LSTMs), Transformers, and a hybrid CNN-RNN model—against the early detection of cancer and neurological diseases. Public datasets comprising histopathological images, EEG signals, and MRI scans were utilized for training and testing the models. Essential preprocessing methods, including normalization, augmentation, and noise removal, were employed to enhance model performance with regard to both spatial and sequential data types.
The performance of models was evaluated using performance measures such as accuracy, precision, recall, F1-score, and ROC-AUC. The results of experiments demonstrate that the Transformer and Hybrid CNN-RNN models surpassed the performance of other models on both disease classifications with detection accuracies of over 92%. The results point to the efficacy of multi-context learning methods, which have the capability of learning both spatial and temporal features in complicated biomedical data simultaneously.
The research illustrates that deep learning can play a substantial role in enhancing early disease diagnosis by providing scalable, effective, and precise diagnostic solutions. Furthermore, it lays the foundation for the future incorporation of explainable artificial intelligence, multi-modal data fusion, and implementation of AI models in real-time clinical environments. Through connecting machine learning advances with practical healthcare applications, the research is building intelligent systems to assist clinicians in making life-critical and timely decisions
Downloads
1. Wulczyn, E., Steiner, D. F., Xu, Z., Sadhwani, A., Wang, H., Flament-Auvigne, I., ... & Stumpe, M. C. (2020). Deep learning-based survival prediction for multiple cancer types using histopathology images. PloS one, 15(6), e0233678.
2. Olumuyiwa, B. I., Han, T. A., & Shamszaman, Z. U. (2024). Enhancing Cancer Diagnosis with Explainable & Trustworthy Deep Learning Models. arXiv preprint arXiv:2412.17527.
3. Wang, A., Hai, R., Rider, P. J., & He, Q. (2022). Noncoding RNAs and deep learning neural network discriminate multi-cancer types. Cancers, 14(2), 352.
4. Liu, S., Liu, S., Cai, W., Pujol, S., Kikinis, R., & Feng, D. (2014, April). Early diagnosis of Alzheimer's disease with deep learning. In 2014 IEEE 11th international symposium on biomedical imaging (ISBI) (pp. 1015-1018). IEEE.
5. Tandel, G. S., Biswas, M., Kakde, O. G., Tiwari, A., Suri, H. S., Turk, M., ... & Suri, J. S. (2019). A review on a deep learning perspective in brain cancer classification. Cancers, 11(1), 111.
6. Gaur, L., Bhandari, M., Razdan, T., Mallik, S., & Zhao, Z. (2022). Explanation-driven deep learning model for prediction of brain tumour status using MRI image data. Frontiers in genetics, 13, 822666.
7. Bhandari, M., Shahi, T. B., Siku, B., & Neupane, A. (2022). Explanatory classification of CXR images into COVID-19, Pneumonia and Tuberculosis using deep learning and XAI. Computers in Biology and Medicine, 150, 106156.
8. Sinha, U., Rao, J. D. P., Swarnkar, S. K., & Tamrakar, P. K. (2023). Advancing Early Cancer Detection with Machine Learning: A Comprehensive Review of Methods and Applications. Multimedia Data Processing and Computing, 165-174.
9. Hussain, S. K., Ramay, S. A., Abbas, T., Kaleem, M., & Khanzada, A. (2024). Ocular Diseases Detection Using Machine Learning, Deep Learning and Artificial Intelligence Based Techniques. Journal of Computing & Biomedical Informatics, 8(01).
10. Pandey, S., & Bansal, S. (2023, March). Brain Cancer Detection Using Deep Learning (Special Session “Digital Transformation Era: Role of Artificial Intelligence, IOT and Blockchain”). In International Conference on Advances in IoT and Security with AI (pp. 337-349). Singapore: Springer Nature Singapore.
11. Venugopalan, J., Tong, L., Hassanzadeh, H. R., & Wang, M. D. (2021). Multimodal deep learning models for early detection of Alzheimer’s disease stage. Scientific reports, 11(1), 3254.
12. Gupta, A., Koul, A., & Kumar, Y. (2022, February). Pancreatic cancer detection using machine and deep learning techniques. In 2022 2nd International Conference on Innovative Practices in Technology and Management (ICIPTM) (Vol. 2, pp. 151-155). IEEE.
13. Koul, A., Bawa, R. K., & Kumar, Y. (2024). An analysis of deep transfer learning-based approaches for prediction and prognosis of multiple respiratory diseases using pulmonary images. Archives of Computational Methods in Engineering, 31(2), 1023-1049.
14. Ahsan, M. M., Luna, S. A., & Siddique, Z. (2022, March). Machine-learning-based disease diagnosis: A comprehensive review. In Healthcare (Vol. 10, No. 3, p. 541). MDPI.
15. Hamash, K. (2023). Comprehensive Clinical Literature Review of Managing Bone Metastases in Breast Cancer: Focus on Pain and Skeletal-Related Events. Number 6/December 2023, 27(6), 615-628.
16. Khan, P., Kader, M. F., Islam, S. R., Rahman, A. B., Kamal, M. S., Toha, M. U., & Kwak, K. S. (2021). Machine learning and deep learning approaches for brain disease diagnosis: principles and recent advances. Ieee Access, 9, 37622-37655.
17. Gautam, R., & Sharma, M. (2020). Prevalence and diagnosis of neurological disorders using different deep learning techniques: a meta-analysis. Journal of medical systems, 44(2), 49.
18. Hunter, B., Hindocha, S., & Lee, R. W. (2022). The role of artificial intelligence in early cancer diagnosis. Cancers, 14(6), 1524.
19. Valliani, A. A. A., Ranti, D., & Oermann, E. K. (2019). Deep learning and neurology: a systematic review. Neurology and therapy, 8(2), 351-365.
20. Xie, S., Yu, Z., & Lv, Z. (2021). Multi-disease prediction based on deep learning: a survey. Computer Modeling in Engineering & Sciences, 128(2), 489-522.
21. Singh, P., Singh, N., Singh, K. K., & Singh, A. (2021). Diagnosing of disease using machine learning. In Machine learning and the internet of medical things in healthcare (pp. 89-111). Academic Press.
22. Siuly, S., & Zhang, Y. (2016). Medical big data: neurological diseases diagnosis through medical data analysis. Data Science and Engineering, 1, 54-64.
23. Vatansever, S., Schlessinger, A., Wacker, D., Kaniskan, H. Ü., Jin, J., Zhou, M. M., & Zhang, B. (2021). Artificial intelligence and machine learning‐aided drug discovery in central nervous system diseases: State‐of‐the‐arts and future directions. Medicinal research reviews, 41(3), 1427-1473.
24. Senturk, Z. K. (2020). Early diagnosis of Parkinson’s disease using machine learning algorithms. Medical hypotheses, 138, 109603.
25. Kumar, Y., Gupta, S., Singla, R., & Hu, Y. C. (2022). A systematic review of artificial intelligence techniques in cancer prediction and diagnosis. Archives of Computational Methods in Engineering, 29(4), 2043-2070.
Copyright (c) 2025 Sahil Kumar

This work is licensed under a Creative Commons Attribution 4.0 International License.