Vitamin Deficiency Detection using Image Processing through Dermatological Symptoms

This study proposes a way to detect vitamin deficiency by combining machine learning and image processing. Computer vision enables the system to recognise visual symptoms of specific vitamin deficiencies. The recommended approach is that the entire procedure can be subdivided into specific key steps, which are initiated from image acquisition, followed by the image preprocessing steps used to enhance their quality. It catches the confusing patterns that are directed toward various abnormalities through a pretrained Convolutional Neural Network (CNN) model. Finally, with such patterns at hand, the categorisation takes place, which in turn helps to identify specific susceptibilities.Through extensive experimentation across a diverse dataset, the system demonstrates remarkable accuracy in detecting deficiency. Its non-invasive nature permits early screening. This proves its potential for widespread implementation and directions for future enhancement, such as dataset expansion and exploration of other advanced architectures apart from CNN. With its promising capabilities, this approach represents a significant stride towards enhancing healthcare diagnostics and preventive measures related to vitamin deficiencies.