• Kondapalii Bhargavi
• M.Naga Keerthi

A wide spectrum of vitamin deficiencies can show one or more visually distinguishable symptoms and indications that appear in multiple locations in the human body. The application provides individuals with the capability to diagnose their possible vitamin deficiencies without the need to provide blood samples through the analysis of photos taken of their eyes, lips, tongue, and nails. This process is implemented using the deep learning based CNN algorithm. Here we have considered the dataset of eyes, lips, tongue and lips. Once after the consideration of dataset, the pre-processing is performed and then CNN algorithm is used to train the data. Once after the training, model is saved and the testing is performed using the OpenCV.

Vitamin deficiency, deep learning, CNN, OpenCV INTRODUCTION, Pre-processing, Visually distinguishable symptoms, Diagnosis, Model, Dataset.

"VITAMIN DEFICIENCY DETECTION USING IMAGE PROCESSING AND NEURAL NETWORK", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 7, page no.d396-d404, July-2024, Available :http://www.jetir.org/papers/JETIR2407339.pdf

"VITAMIN DEFICIENCY DETECTION USING IMAGE PROCESSING AND NEURAL NETWORK", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 7, page no. ppd396-d404, July-2024, Available at : http://www.jetir.org/papers/JETIR2407339.pdf