Linear Gaussian Kernel Cycle Generative Adversarial Networks based Transformer Learning for Early-Stage Stroke Prediction with Parkinson and Skin Disease

Technical Summary: Linear Gaussian Kernel Cycle Generative Adversarial Networks based Transformer Learning for Early-Stage Stroke Prediction

Problem Statement
Stroke remains a leading cause of global mortality and long-term disability, making early prediction critical for timely medical intervention. While neuro-related conditions like Parkinson's disease and dermatological conditions such as skin wrinkles (e.g., Psoriasis, Eczema, Lupus) may offer valuable indicators of stroke risk, existing deep learning models face significant challenges. Current approaches often struggle to achieve high prediction accuracy while maintaining low computational time. Furthermore, many existing methods fail to adequately address image quality issues, such as noise artifacts, or lack robust mechanisms for feature extraction from complex medical images like Parkinson's drawings and skin lesion datasets.

Methodology
The paper proposes a novel framework termed Linear Gaussian Kernel Cycle Generative Adversarial Networks based Transformer Learning (LGKCGAN-TL). The architecture integrates five distinct processing stages: image acquisition, pre-processing, data augmentation, feature extraction, and classification.

1. Image Acquisition: The model utilizes two specific datasets: the Parkinson's Drawings dataset (containing spiral and wave drawings from healthy individuals and Parkinson's patients) and the Dermnet dataset (focusing on skin diseases like Psoriasis, Eczema, and Lupus).
2. Pre-processing (Camargo's Index Box Linear Denoising): To enhance image quality and reduce noise artifacts, the authors employ Camargo's index box linear denoising. This technique uses a box filter to average pixel values within a window. It calculates a statistical "Camargo's index" to distinguish between normal pixels and noise based on their dependency on the local mean. Noisy pixels are replaced with the average of neighboring pixels, thereby maximizing the Peak Signal-to-Noise Ratio (PSNR).
3. Data Augmentation (Gaussian Kernel Cycle GAN): To address data scarcity and improve model robustness, the framework uses Gaussian Kernel Cycle Generative Adversarial Networks (Cycle GANs). This involves two generators and two discriminators arranged in a ring network.
   • Generator A transforms input images into a synthetic domain using a Gaussian kernel function to generate new pixel values.
   • Generator B attempts to reconstruct the original image from the synthetic domain.
   • The system minimizes adversarial loss (distinguishing real vs. fake) and cycle consistency loss (ensuring the original image is preserved after a round-trip transformation), effectively expanding the dataset size.
4. Feature Extraction and Classification (Rosenthal Correlative Transformer Learning): The core classification engine is a Rosenthal Correlative Transformer Learning model.
   • Patch Embedding: Input images are divided into fixed-size non-overlapping patches.
   • Positional Encoding: Sine and cosine functions are applied to encode the spatial position of each patch.
   • Transformer Encoder: Self-attention mechanisms process these patches to capture global dependencies, extracting geometric, texture, color, and shape features.
   • Rosenthal Correlation: In the fully connected dense layer, a Rosenthal correlation function analyzes the relationship between extracted feature vectors and testing feature vectors.
   • Classification: A sigmoid activation function outputs a binary classification (stroke risk vs. no risk) based on the correlation analysis.

Key Contributions
The paper outlines the following specific contributions:

• Development of the LGKCGAN-TL model, which integrates preprocessing, augmentation, and transformer-based learning for stroke prediction.
• Implementation of Camargo's index box linear denoising to improve image quality and PSNR by removing noise artifacts.
• Utilization of Gaussian Kernel Cycle GANs to augment the dataset, generating synthetic samples to enhance model robustness.
• Application of Rosenthal Correlative Transformer Learning to extract complex spatial features and perform accurate risk classification with reduced prediction time.
• Comprehensive experimental evaluation comparing the proposed model against existing GA-BiLSTM and DCNN models.

Experimental Results
The model was evaluated using the Parkinson's Drawings and Dermnet datasets, with performance measured against GA-BiLSTM and DCNN baselines. Key findings include:

• Image Quality: The LGKCGAN-TL model improved PSNR by approximately 8–9% compared to baseline models across both datasets.
• Accuracy: The model achieved an average prediction accuracy of 98–99%.
• Precision and Recall: It attained a precision of 98.5%, a recall of 97.8%, and an F1-score of 98%.
• Specificity: The specificity reached 93%, representing an improvement of 11–12% over GA-BiLSTM and 34% over DCNN.
• Prediction Time: The stroke prediction time was reduced to approximately 55 ms, a decrease of 7–16% compared to the baseline models.
• Confusion Matrix & ROC: The model demonstrated high diagnostic ability, correctly identifying 825 true positives and 100 true negatives in the Parkinson's dataset (1000 samples). The ROC curve analysis confirmed superior performance over existing methods.

Significance and Claims
The authors claim that the LGKCGAN-TL model offers a significant advancement in early-stage stroke prediction by effectively leveraging multimodal data (Parkinson's drawings and skin disease images). The significance of the work lies in its hybrid approach:

1. Enhanced Data Quality: The denoising technique ensures that the model learns from high-quality inputs.
2. Robustness: The GAN-based augmentation addresses the limitation of small medical datasets.
3. Efficiency and Accuracy: The transformer-based architecture, combined with Rosenthal correlation, allows for the capture of long-range dependencies and global features, resulting in higher accuracy and faster inference times compared to traditional CNNs and GA-BiLSTM models.

The paper concludes that while the model shows promise, future work should address limitations such as the reliance on specific datasets and the lack of multimodal clinical evidence (e.g., MRI, CT scans, and patient health records). The authors suggest expanding the model to include federated learning and real-time Internet of Medical Things (IoMT) frameworks to improve scalability.