AI-Driven Cloud-Based Deep Learning for Predictive Healthcare Analytics: Enhancing Disease Diagnosis with CNNs in Medical Imaging

Abstract

Brain tumors pose a significant challenge in medical diagnostics, requiring early and accurate detection for effective treatment. Traditional diagnostic methods rely heavily on manual assessment of MRI scans, which can be time-consuming and prone to human error. Recent advancements in deep learning and cloud computing have enabled the development of automated, scalable, and high-accuracy medical imaging solutions. This study presents an AI-Driven Cloud-Based Deep Learning Framework for predictive healthcare analytics, integrating Convolutional Neural Networks (CNNs) with cloud computing to enhance scalability and computational efficiency. The Brain Tumor MRI Dataset from Kaggle is used, and extensive data preprocessing techniques such as noise reduction, normalization, and contrast enhancement are applied to improve image quality. The proposed model achieves 97.2% accuracy, outperforming traditional methods like SVM (88.5%) and Random Forest (91.3%), with an AUC-ROC score of 98.3%, demonstrating superior classification capability. Cloud-based deployment significantly reduces training time to 45 minutes compared to over 3 hours on local systems, ensuring rapid and resource-efficient processing of large MRI datasets. The findings highlight the potential of AI-driven, cloud-integrated deep learning for real-time, scalable, and high-accuracy medical diagnostics, offering a transformative approach to brain tumor detection in predictive healthcare analytics. The proposed AI-driven cloud-based deep learning framework achieves high accuracy in brain tumor classification while significantly reducing training time through scalable cloud computing. This approach enhances diagnostic efficiency, making advanced medical imaging solutions more accessible and effective.