An Intelligent Deep Learning Framework For Integrated Eye And Cancer Disease Detection: Social Innovation for Healthcare

Cancer screening is a highly contentious subject in the field of medicine. An analysis of publicly available data reveals numerous points of view, often based on a limited amount of valid information. The ideal age ranges for mammography screening, as well as the value of the procedure itself, remain debated. Similarly, the usefulness of lung or prostate cancer screening is still a question. Recommendations and decisions for cancer screening should be grounded in reliable evidence rather than good intentions, presumptions, or supposition. Understanding the underlying ideas and presumptions is essential in order to fully understand the present challenges related to testing for blood, prostate, and breast cancers. The probable financial, legal, and radiation safety impacts of entire-body CT or PET cancer screening will be covered in this paper. The patient’s body is scanned and images are preserved. Now using PET/CT abnormalities or cancer tissues are detected in the images. The body parts affected by cancer are localized. Eye disease screening should also be included in this. Similar to cancer screenings, eye disease screenings must be based on solid evidence. Detecting conditions such as glaucoma, macular degeneration, and diabetic retinopathy early can significantly impact patient outcomes. Incorporating machine learning algorithms in eye disease screenings can enhance the accuracy and efficiency of predictions, potentially leading to better patient care and management. By understanding and addressing these challenges, we can improve the reliability and effectiveness of both cancer and eye disease screenings.