Optimizing the early diagnosis of neurological disorders through the application of machine learning for predictive analytics in medical imaging.
Authors
Affiliations (8)
Affiliations (8)
- Department of Mechanical Engineering, Jawaharlal Nehru Technological University, Kakinada, 533003, Andhra Pradesh, India.
- Software Development Team Lead at Paycom, Master of Computer Science, Oklahoma Christian University, Edmond, Oklahoma, 73013, USA.
- Department of Business Analytics, Jaipuria Institute of Management, Noida, Uttar Pradesh, 201309, India.
- Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Hyderabad, 500075, Telangana, India.
- School of Engineering, Anurag University, Hyderabad, Telangana, 500088, India.
- Department of Computer Science, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al-Kharj, 16278, Saudi Arabia.
- Department of Computer Science and Engineering, PSN College of Engineering and Technology, Tirunelveli, 627451, Tamil Nadu, India. [email protected].
- Department of Artificial Intelligence and Data Science, Mahendra Engineering College, Mallasamudram, Namakkal, Tamil Nadu, 637503, India. [email protected].
Abstract
Early diagnosis of Neurological Disorders (ND) such as Alzheimer's disease (AD) and Brain Tumors (BT) can be highly challenging since these diseases cause minor changes in the brain's anatomy. Magnetic Resonance Imaging (MRI) is a vital tool for diagnosing and visualizing these ND; however, standard techniques contingent upon human analysis can be inaccurate, require a long-time, and detect early-stage symptoms necessary for effective treatment. Spatial Feature Extraction (FE) has been improved by Convolutional Neural Networks (CNN) and hybrid models, both of which are changes in Deep Learning (DL). However, these analysis methods frequently fail to accept temporal dynamics, which is significant for a complete test. The present investigation introduces the STGCN-ViT, a hybrid model that integrates CNN + Spatial-Temporal Graph Convolutional Networks (STGCN) + Vision Transformer (ViT) components to address these gaps. The model causes the reference to EfficientNet-B0 for FE in space, STGCN for FE in time, and ViT for FE using AM. By applying the Open Access Series of Imaging Studies (OASIS) and Harvard Medical School (HMS) benchmark datasets, the recommended approach proved effective in the investigations, with Group A attaining an accuracy of 93.56%, a precision of 94.41% and an Area under the Receiver Operating Characteristic Curve (AUC-ROC) score of 94.63%. Compared with standard and transformer-based models, the model attains better results for Group B, with an accuracy of 94.52%, precision of 95.03%, and AUC-ROC score of 95.24%. Those results support the model's use in real-time medical applications by providing proof of the probability of accurate but early-stage ND diagnosis.