Advancing Breast Cancer Detection: Integrating IoT and Deep Learning in Next-Generation Healthcare
Keywords:
Early-Stage Breast Cancer Detection, Intelligent Systems And Deep Learning, Telehealth And Healthcare Automation, Electronic Health Records (EHRs), Public Health And Global Challenges, Internet Of Things (IoT), Machine LearningAbstract
Background: Breast cancer remains a significant global health concern, and traditional diagnostic methods sometimes fall short, particularly in early detection. The rise of telehealthcare underscores the need for innovative solutions.
Objective: This study explores the potential of combining intelligent systems, such as thermal imaging cameras, with deep learning techniques, especially convolutional neural networks (CNNs), to revolutionize and improve breast cancer detection compared to traditional risk models.
Method: The study proposes a novel predictive healthcare system utilizing the Internet of Things (IoT) and Electronic Health Records (EHRs). This system aims to achieve real-time, automated breast cancer detection in diverse healthcare settings, including institutional care facilities, hospitals, and even schools. Additionally, the system seeks to facilitate early detection of potentially other health conditions by leveraging the combined power of IoT and CNN deep learning. This would involve developing user-friendly EHRs accessible to patients' healthcare providers within their institutions or nationwide.
Results: The research delves into the specific procedures and techniques required to develop this proposed comprehensive cancer detection system.
Main Findings: The study investigates whether this approach offers a potential solution to address the global challenges associated with breast cancer and contribute to improved patient well-being.
Conclusion: This research investigates the potential of a novel healthcare system integrating intelligent systems and deep learning for early breast cancer detection and potentially other health conditions. Further research is needed to evaluate the feasibility and effectiveness of this approach in real-world settings.
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