Low-cost imaging using mobile phone to detect and diagnose disease in deep learning

Introduction: This Artificial intelligence increases the ability of Healthcare to better understand the day-to-day pattern and needs of the patient which will ultimately provide better guidance and support for staying healthy. We use AI to detect and diagnose disease using a fluorescent imaging system or white light photography which is low at cost. Methods: Artificial intelligence is poised to make an incredible impact on our life in the future today however we still face new challenges in directing and diagnosing several life-threatening illnesses such as infectious diseases and cancer. Thousands of people lose their lives every year due to liver and oral cancer. The best way to help these patients is to perform early detection and diagnosis of these diseases. Expert physicians initially suggest very expensive Medical Technologies such as fluorescence imaging, CT Scan, MRI scan to be performed for diagnosis. This is very research resource intensive process requiring both expert physician and expensive medical imaging technologies. It would require 10000 of medical images for traditional AI architecture instead of using an orthodox approach. Here, we developed a technology that requires as few as 50 images for these high resolutions, but standard photographs acquired from DSLR cameras and mobile phones and provide diagnosis and can even use photos taken on doctor’s cell phones to provide a diagnosis. This reduces the number of images required to train AI algorithm which can in turn reduce the use of expensive medical imaging technologies. To screen patients from a single image, we can extract millions of information packet that includes colors, pixels, geometry rendering of the image on the medical. We convert one image into billions of training data points, massively reducing the amount of data needed for training the AI. This method requires only 50 images to train our algorithm and can accept some very simple white light photographs from the patient instead of expensive medical imaging technologies. Using this way, we can analyze the medical information that could lead to earlier detection of life-threatening illnesses and bring AI assisted diagnosis to more health care settings worldwide. Results & Discussions: Many lose their lives due to liver and oral cancer. Expert Physician uses very expensive medical imaging technology such as Computed tomography magnetic resonance imaging to be performed. So we use AI to detect and fluorescent imaging systems or white light photography which is low at cost. Conclusions: Introducing AI on health care reduces cost and accurate detection and analysis of a patient. AI makes it easier for system to scan, thereby detecting cancer and vascular diseases in advance. It also helps in predicting the health risks people may face in future. Key words: Light emitting diode, White Light Photography, Fluorescent imaging System. References 1. https://web.media.mit.edu/~pratiks/combined-classification/ 2. https://web.media.mit.edu/~pratiks/biomarker-imaging/biomarker-imaging.htm