Assistive System to Identify and Manage Lung Cancer.

Lung cancer, a leading cause of cancer-related deaths worldwide, necessitates early detection and effective management tools. This research introduces an assistive system leveraging machine learning to identify risk factors and prioritize treatments based on severity. Critical sociodemographic data, like age, gender, and smoking history, enable accurate risk assessment and personalized profiles. The system adapts to lifestyle changes post-diagnosis, offering tailored healthcare solutions. Integrating Mask RCNN, a deep learning algorithm for medical imaging, enhances lung cancer diagnosis precision and treatment strategies. The user-friendly interface allows healthcare providers, caregivers, and patients easy access via mobile or computer applications. The implementation includes natural language processing for questionnaires and data preprocessing. The system's design consists of three components: front-end interface, back-end server, and Mask R-CNN for tumor identification. Correlation analysis for patient prioritization to generate a list. The proposed system aims to revolutionize lung cancer care, delivering accurate risk assessments, personalized treatment plans, and continuous monitoring, ultimately improving patient outcomes and saving lives. [ABSTRACT FROM AUTHOR]