Experimental Evaluation of Brain Cerebral Palsy Disease Prediction Using Artificial Intelligence Assisted Learning Methodology

Cerebral Palsy (CP) is a complex neurodevelopmental disorder affecting movement, muscle tone, and posture. Early detection and intervention are crucial for managing CP effectively. This abstract presents a novel approach for predicting CP using Artificial Intelligence Assisted Learning Methodology (AIALM) with a Cascaded LeNet architecture. The proposed methodology leverages advancements in artificial intelligence and deep learning to enhance the accuracy and efficiency of CP prediction. The Cascaded LeNet architecture, inspired by convolutional neural networks (CNNs), is designed to extract relevant features from brain images associated with CP. Data preprocessing techniques are applied to prepare the input data, including normalization and augmentation, ensuring the robustness of the model. The AIALM framework integrates the cascaded LeNet model with intelligent learning mechanisms, enabling continuous adaptation and optimization based on incoming data. This dynamic learning approach enhances the model's ability to adapt to diverse patient profiles and imaging variations, ultimately improving prediction accuracy. Extensive experimentation and evaluation demonstrate the effectiveness of the proposed methodology. The model achieves an impressive accuracy of 96.4% in predicting CP from brain images, outperforming existing approaches. Furthermore, the model's performance is validated through rigorous cross-validation and comparison with clinical assessments.