ISSN (Online): 2321-3418
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Medical Sciences and Pharmacy
Open Access

Early Disease Detection Using AI: A Deep Learning Approach to Predicting Cancer and Neurological Disorders

DOI: 10.18535/ijsrm/v13i04.mp02· Pages: 2136-2155· Vol. 13, No. 04, (2025)· Published: April 3, 2025
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Abstract

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

Keywords

Early Disease DetectionDeep LearningCancer PredictionNeurological Disorder DiagnosisConvolutional Neural Networks (CNN)Transformer ModelsMedical ImagingEEG Signal Analysis. 1. Introduction

References

  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.Google Scholar ↗
  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.Google Scholar ↗
  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.Google Scholar ↗
  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.Google Scholar ↗
  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.Google Scholar ↗
  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.Google Scholar ↗
  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.Google Scholar ↗
  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.Google Scholar ↗
  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).Google Scholar ↗
  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.Google Scholar ↗
  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.Google Scholar ↗
  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.Google Scholar ↗
  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.Google Scholar ↗
  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.Google Scholar ↗
  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.Google Scholar ↗
  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.Google Scholar ↗
  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.Google Scholar ↗
  18. Hunter, B., Hindocha, S., & Lee, R. W. (2022). The role of artificial intelligence in early cancer diagnosis. Cancers, 14(6), 1524.Google Scholar ↗
  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.Google Scholar ↗
  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.Google Scholar ↗
  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.Google Scholar ↗
  22. Siuly, S., & Zhang, Y. (2016). Medical big data: neurological diseases diagnosis through medical data analysis. Data Science and Engineering, 1, 54-64.Google Scholar ↗
  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.Google Scholar ↗
  24. Senturk, Z. K. (2020). Early diagnosis of Parkinson’s disease using machine learning algorithms. Medical hypotheses, 138, 109603.Google Scholar ↗
  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.Google Scholar ↗
Author details
Sahil Kumar
Master of Science DePaul University
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