Deep Learning technique and parallel optimization algorithm for intelligent pattern recognition

Abstract

Misdiagnosis poses a significant challenge within the healthcare sector, carrying potentially severe consequences for patients, including delayed or inappropriate treatment, unnecessary me dical procedures, emotional distress, financial burdens, and legal repercussions. To address this issue, we propose the utilization of deep learning algorithms to enhance the precision of medi cal diagnoses. However, the development of accurate deep learning models for medical purposes necessitates substantial quantities of top-quality data, a resource that can be challenging for individual healthcare entities to acquire. Consequently, there is a need to aggregate data from various sources to create a diverse dataset suitable for effective model training. Nevertheless, the sharing of medical data across different healthcare sectors is fraught with security concerns due to the sensitive nature of the information and stringent privacy regulations. To tackle these complex challenges, we advocate for the adoption of Blockchain technology, which offers a se cure, decentralized, and privacy-centric approach to sharing locally trained deep learning models, thereby obviating the need to exchange raw data. Our proposed technique, known as model en sembling, combines the strengths of multiple local deep learning models by aggregating their weights to construct a unified global model. This global model enables accurate diagnosis of intricate medical conditions across various locations while safeguarding patient privacy and data integrity. Our research serves as a testament to the efficacy of this approach, achieving high accuracy rates in the diagnosis of three diseases (accuracy of 97.44 % for the Breast Cancer, 97.14 % for the Diabetes, and 98.51 % for the Lung Cancer) that surpass those of individual local models. Furthermore, we have successfully developed a multi-diagnosis application as an outcome of this innovative methodology