AI-BASED MEDICAL IMAGE ANALYSIS FOR EARLY DETECTION OF NEUROLOGICAL DISORDERS USING DEEP LEARNING
Abstract
Neurological disorders such as Alzheimer’s disease (AD), Parkinson’s disease (PD), and brain tumors remain among the primary contributors to global disability and mortality. Early and precise diagnosis is essential for effective intervention and improved patient outcomes. However, conventional diagnostic methods rely heavily on manual interpretation of neuroimaging by radiologists, which can be time-consuming, subjective, and susceptible to human error. The emergence of artificial intelligence (AI), particularly deep learning (DL), offers a transformative solution through automated and high-accuracy medical image analysis. This study proposes an AI-driven diagnostic framework that leverages EfficientNetB0, a lightweight yet high-performing convolutional neural network (CNN), to classify neurological conditions using brain MRI and CT scans. The model was trained and fine-tuned on a labeled dataset comprising three categories: Alzheimer’s disease, Parkinson’s disease, and healthy controls. It achieved an overall classification accuracy of 95%, demonstrating its effectiveness in differentiating between pathological and non-pathological cases. The model reported a precision, recall, and F1-score of 0.97 for AD, a recall of 0.98 for control cases, and a precision of 0.96 with a recall of 0.85 for PD. Additionally, the area under the ROC curve (AUC) was 0.98 for AD, 0.95 for controls, and 0.92 for PD, indicating strong discriminative performance. These findings highlight the potential of EfficientNetB0 as a scalable, efficient, and accurate tool for supporting early detection and diagnosis of neurological disorders in clinical practice. This work contributes to advancing AI-assisted healthcare solutions aimed at improving diagnostic speed and consistency in neuroimaging analysis.
References
Francisco Santos, D. (2023). Advancing Automated Diagnosis: Convolutional Neural Networks for Alzheimers Disease Classification through MRI Image Processing. https://doi.org/10.36227/techrxiv.23002007.v1
Gauriau, R., Bizzo, B. C., Kitamura, F. C., Landi Junior, O., Ferraciolli, S. F., Macruz, F. B. C., Sanchez, T. A., Garcia, M. R. T., Vedolin, L. M., Domingues, R. C., Gasparetto, E. L., & Andriole, K. P. (2021). A Deep Learning-based Model for Detecting Abnormalities on Brain MR Images for Triaging: Preliminary Results from a Multisite Experience. Radiology. Artificial Intelligence, 3(4), e200184. https://doi.org/10.1148/ryai.2021200184
Hazarika, R. A., Kandar, D., & Maji, A. K. (2022). An experimental analysis of different Deep Learning based Models for Alzheimers Disease classification using Brain Magnetic Resonance Images. Journal of King Saud University - Computer and Information Sciences, 34(10), 85768598. https://doi.org/10.1016/j.jksuci.2021.09.003
Iqbal, M. S., Belal Bin Heyat, M., Parveen, S., Ammar Bin Hayat, M., Roshanzamir, M., Alizadehsani, R., Akhtar, F., Sayeed, E., Hussain, S., Hussein, H. S., & Sawan, M. (2024). Progress and trends in neurological disorders research based on deep learning. Computerized Medical Imaging and Graphics, 116, 102400. https://doi.org/10.1016/j.compmedimag.2024.102400
Kaur, I., & Sachdeva, R. (2025). Prediction Models for Early Detection of Alzheimer: Recent Trends and Future Prospects. Archives of Computational Methods in Engineering. https://doi.org/10.1007/s11831-025-10246-3
Kumar, S. (2025). Early Disease Detection Using AI: A Deep Learning Approach to Predicting Cancer and Neurological Disorders. International Journal of Scientific Research and Management (IJSRM), 13(04), 21362155. https://doi.org/10.18535/ijsrm/v13i04.mp02
Md Ruhul Amin. (2023). Alzheimer_Parkinson_Disease_Classification [Dataset]. Kaggle online data repository. https://www.kaggle.com/code/ruhul77/alzheimer-parkinson-disease-classification
Mehmood, A., Shahid, F., Khan, R., Ibrahim, M. M., & Zheng, Z. (2024). Utilizing Siamese 4D-AlzNet and Transfer Learning to Identify Stages of Alzheimers Disease. Neuroscience, 545, 6985. https://doi.org/10.1016/j.neuroscience.2024.03.007
Mehmood, K., Bao, Y., Saifullah, Cheng, W., Khan, M. A., Siddique, N., Abrar, M. M., Soban, A., Fahad, S., & Naidu, R. (2022). Predicting the quality of air with machine learning approaches: Current research priorities and future perspectives. Journal of Cleaner Production, 379, 134656. https://doi.org/10.1016/j.jclepro.2022.134656
Nguyen, D., Nguyen, H., Ong, H., Le, H., Ha, H., Duc, N. T., & Ngo, H. T. (2022). Ensemble learning using traditional machine learning and deep neural network for diagnosis of Alzheimers disease. IBRO Neuroscience Reports, 13, 255263. https://doi.org/10.1016/j.ibneur.2022.08.010
Oise, G. P., Oyedotun, S. A., Nwabuokei, O. C., Babalola, A. E., & Unuigbokhai, N. B. (2025). ENHANCED PREDICTION OF CORONARY ARTERY DISEASE USING LOGISTIC REGRESSION. FUDMA JOURNAL OF SCIENCES, 9(3), 201208. https://doi.org/10.33003/fjs-2025-0903-3263
Priyatama, A., Sari, Z., & Azhar, Y. (2023). Deep Learning Implementation using Convolutional Neural Network for Alzheimers Classification. Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 7(2), 310217. https://doi.org/10.29207/resti.v7i2.4707
Qiu, S., Miller, M. I., Joshi, P. S., Lee, J. C., Xue, C., Ni, Y., Wang, Y., De Anda-Duran, I., Hwang, P. H., Cramer, J. A., Dwyer, B. C., Hao, H., Kaku, M. C., Kedar, S., Lee, P. H., Mian, A. Z., Murman, D. L., OShea, S., Paul, A. B., Kolachalama, V. B. (2022). Multimodal deep learning for Alzheimers disease dementia assessment. Nature Communications, 13(1), 3404. https://doi.org/10.1038/s41467-022-31037-5
Shan Wang, Jiejie Zhang, Haitao Zhang, Yihan Yang, & Ya Wen. (2024). Exploration of mitochondrial autophagy related genes in the diagnosis modelconstruction and molecular marker mining of Alzheimers disease based on multiomicsintegration. Cellular and Molecular Biology, 70(6), 114121. https://doi.org/10.14715/cmb/2024.70.6.18
Singh, D. P., Kaushik, B., Khan, Y. F., Chadha, A., Mahajan, A., Jandwani, A., & Narula, G. S. (2024). Emerging Trends of Artificial Intelligence in Detecting Neurodegeneration. In S. Tanwar, P. K. Singh, M. Ganzha, & G. Epiphaniou (Eds.), Proceedings of Fifth International Conference on Computing, Communications, and Cyber-Security (Vol. 991, pp. 591601). Springer Nature Singapore. https://doi.org/10.1007/978-981-97-2550-2_42
Tanveer, M., Richhariya, B., Khan, R. U., Rashid, A. H., Khanna, P., Prasad, M., & Lin, C. T. (2020). Machine Learning Techniques for the Diagnosis of Alzheimers Disease: A Review. ACM Transactions on Multimedia Computing, Communications, and Applications, 16(1s), 135. https://doi.org/10.1145/3344998
Termine, A., Fabrizio, C., Caltagirone, C., Petrosini, L., & on behalf of the Frontotemporal Lobar Degeneration Neuroimaging Initiative. (2022). A Reproducible Deep-Learning-Based Computer-Aided Diagnosis Tool for Frontotemporal Dementia Using MONAI and Clinica Frameworks. Life, 12(7), 947. https://doi.org/10.3390/life12070947
Vij, R., & Arora, S. (2022). Computer Vision with Deep Learning Techniques for Neurodegenerative Diseases Analysis Using Neuroimaging: A Survey. In A. Khanna, D. Gupta, S. Bhattacharyya, A. E. Hassanien, S. Anand, & A. Jaiswal (Eds.), International Conference on Innovative Computing and Communications (Vol. 1388, pp. 179189). Springer Singapore. https://doi.org/10.1007/978-981-16-2597-8_15
Yiting Hou, Lihua Dong, & Lihua Cao. (2023). Saxagliptin reduces the injury of Alzheimers disease cell model by down-regulating the expression of miR-483-5p. Cellular and Molecular Biology, 69(12), 188193. https://doi.org/10.14715/cmb/2023.69.12.30
Zhou, Q., Wang, J., Yu, X., Wang, S., & Zhang, Y. (2023). A Survey of Deep Learning for Alzheimers Disease. Machine Learning and Knowledge Extraction, 5(2), 611668. https://doi.org/10.3390/make5020035
Copyright (c) 2025 FUDMA JOURNAL OF SCIENCES
This work is licensed under a Creative Commons Attribution 4.0 International License.
FUDMA Journal of Sciences
