Design of a deep fusion model for early Parkinson's disease prediction using handwritten image analysis.

Parkinson's Disease (PD) is a deteriorating condition that mostly affects older people. The lack of conclusive treatment for PD makes diagnosis very challenging. However, using patterns like tremors for early diagnosis, handwriting analysis has become a useful diagnostic technique. This work aims to improve early PD diagnosis by proposing a hybrid deep fusion model that blends ResNet-50 and GoogLeNet (RGG-Net). We demonstrated the RGG-Net model in a series of steps such as preprocessing images, ResNet-50 and GoogLeNet models for feature extraction, combining the features using the Adaptive Feature Fusion technique and selecting the relevant features using the attention process, making the models stronger through Hierarchical Ensemble Learning. The grad-CAM technique is used for decision-making in PD prediction. The proposed model is a reliable way to analyze handwritten images using advanced techniques like adaptive feature fusion, hierarchical ensemble learning, and eXplainable Artificial Intelligence. Here, we analyzed ten pre-trained models to determine which model best captures the relevant features for PD classification using handwritten images. The models included are AlexNet, DenseNet-201, SqueezeNet1.1, VGG-16, VGG-19, ResNet-50, ResNet-101, GoogleNet, MobileNetV1, and MobileNetV2. The proposed deep transfer learning model showed an accuracy of 99.12%, outperforming the other state-of-the-art methods, indicating the model's excellence and vigor. The proposed model performs better than all pre-trained models with and without freezing convolutional layers. These results underscore the efficacy of the proposed approach in enhancing accuracy and transparency in Parkinson's disease prediction and the potential of deep learning in promoting early diagnosis.