Advancing personalized diagnosis and treatment using deep learning architecture

Autoimmune disorders (AID) present significant challenges due to their complex etiologies and diverse clinical manifestations. Traditional diagnostic methods, which rely on symptom observation and biomarker detection, often lack specificity and fail to provide personalized treatment options. This study proposes ImmunoNet, a deep learning-based framework that integrates genetic, molecular, and clinical data to enhance the accuracy of autoimmune disease diagnosis and treatment. ImmunoNet leverages convolutional neural networks (CNNs) and multi-layer perceptrons (MLPs) to analyze large-scale datasets, enabling precise disease classification and personalized therapeutic treatment recommendations. The model improves interpretability through explainable AI techniques and enhances privacy via federated learning. Comparative evaluations demonstrate that ImmunoNet outperforms traditional machine learning models, achieving a 98% accuracy rate in predicting autoimmune disorders. By advancing precision medicine in immunology, this approach provides clinicians with a powerful tool for personalized diagnosis and optimized therapeutic strategies.