Blockchain-based Multi-Diagnosis Deep Learning Application for Various Diseases Classification

Misdiagnosis is a critical issue in healthcare, which can lead to severe consequences for patients, in- cluding delayed or inappropriate treatment, unneces- sary procedures, psychological distress, financial bur- den, and legal implications. To mitigate this issue, we propose using deep learning algorithms to improve diag- nostic accuracy. However, building accurate deep learn- ing models for medical diagnosis requires substantial amounts of high-quality data, which can be challeng- ing for individual healthcare sectors or organizations to acquire. Therefore, combining data from multiple sources to create a diverse dataset for efficient train- ing is needed. However, sharing medical data between different healthcare sectors can be problematic from a security standpoint due to sensitive information and privacy laws. To address these challenges, we propose using Blockchain technology to provide a secure, de- centralized, and privacy-respecting way to share locally trained deep learning models instead of the data itself. Our proposed method of model ensembling, which com- bines the weights of several local deep learning models to build a single global model, that enables accurate diagnosis of complex medical conditions across multi- ple locations while preserving patient privacy and data security. Our research demonstrates the effectiveness of this approach in accurately diagnosing three diseases (Breast cancer, Lung cancer, and Diabetes) with high accuracy rates, surpassing the accuracy of local models and building a multi-diagnosis application.