Early Detection and Classification of Vitamin Deficiency Using CNN

Vitamin deficiency remains a major global health concern, affecting millions of people. Traditional techniques of detection and categorization frequently rely on manual assessments and clinical evaluations, which are time-consuming and open to subjective interpretation. In this study, investigated the use of AlexNet CNN algorithm approaches to detect and classify vitamin deficiencies. This research study aims to create predictive models capable of properly recognizing specific deficiencies from patient datasets. The findings are interesting, demonstrating that machine learning approaches can improve diagnostic accuracy. This study helps to further personalized medicine and public health interventions aimed at enhancing early identification and intervention options for people at risk of vitamin deficiency. The proposed model got around 70 percent of accuracy for our vitamin deficiency detection.