Smart Healthcare System for Automated Disease Detection and Cure Recommendation through Medical Imaging

Abstract: The rapid advancement of medical imaging and artificial intelligence has enabled the development of smart healthcare systems for accurate and timely disease detection. This paper proposes an intelligent healthcare framework that utilizes deep learning techniques to automatically analyze medical images such as X-rays, MRI, and CT scans for early diagnosis of diseases. The system employs convolutional neural networks (CNNs) for feature extraction and classification, ensuring high accuracy and efficiency in identifying abnormalities. In addition to disease detection, the proposed model integrates a recommendation module that suggests suitable treatment options and preventive measures based on the diagnosed condition. The system is designed to assist healthcare professionals by reducing diagnostic errors, minimizing workload, and improving decision-making processes. Furthermore, the integration of cloud-based storage ensures secure data management and easy accessibility for both patients and medical practitioners. Experimental results demonstrate that the system achieves reliable performance with faster processing time compared to traditional diagnostic methods. Overall, this smart healthcare solution enhances early diagnosis, supports personalized treatment, and contributes to improved patient outcomes, especially in resource-limited environments.

Keywords: Predictive Analytics, Computer-Aided Diagnosis (CAD), Healthcare Automation, Medical Image Analysis, Early Detection Systems, Personalized Medicine, Cloud-Based Healthcare, Big Data in Healthcare, Diagnostic Accuracy, Pattern Recognition, Neural Networks, Clinical Decision Making, Health Informatics, Remote Patient Monitoring, AI- Based Healthcare