Published Paper Details:

In today's era, early detection of breast cancer is crucial for effective treatment and improved survival rates. Traditional methods of diagnosis often involve invasive procedures and can be prone to human error and subjectivity. This paper presents a predictive analytics model for breast cancer detection leveraging Convolutional Neural Networks (CNNs). The model utilizes a centralized server to store data, which is accessible to both medical professionals and patients via an Android application or website, ensuring secure data transmission. The proposed approach includes advanced mechanisms for accurately identifying malignant tumors from medical imaging, reducing false positives and false negatives. The system is equipped with image processing techniques to enhance detection accuracy, and CNNs are trained on large datasets to recognize patterns indicative of cancerous tissues. Upon detection, immediate notifications are dispatched to medical professionals for further action, enabling timely intervention and treatment. This approach not only streamlines the diagnostic process but also enhances the accuracy and reliability of breast cancer detection, contributing to better patient outcomes. By leveraging deep learning and IoT technology, the solution offers a comprehensive, automated method to manage and secure medical imaging data, ultimately promoting advancements in medical diagnostics and patient care.