Multivitamin Deficiency Detection Using Image Processing and CNN

Abstract

Vitamin andmicronutrientdeficiencies areasignificant globalhealth concern,leadingto various adversehealth consequences. Earlydetectionandinterventionareessentialinaddressingtheseissues.Theprojectintroducesanintelligentsystem that utilizes advanced deep learning techniques to identify and differentiate vitamin deficiencies in human tissue throughimageanalysis.Theapproachinvolvesaninitialstepofimageclustering toseparate andisolateproblemareas fromthe inputimages. The goal is to evaluate the productivity of image segmentation methods, extract relevant characteristics, and compareclassification results with other methods. To accomplish theobjectives,adiversedatasetof facial images is gatheredand preprocessed,focusingonindividualsbothwithandwithoutvisiblesignsofvitamindeficienciesFollowingthis,aCNNalgorithm,inspiredbymodelslikeAlexNet,iscreatedandtrainedusingthepreprocesseddataset.TheCNNisusedtoidentifyandclassifyfeaturesbasedondifferenttypesofvitamindeficiencies,enablinganautomatedandaccurateassessmentbasedonfacialimages.

Introduction

The study focuses on detecting multivitamin deficiencies using image-based analysis and advanced machine learning techniques. Vitamin deficiencies affect key body functions such as immunity, energy production, skin health, and overall well-being. Since the skin reflects internal health, its visual changes can help identify early signs of vitamin imbalance and skin disorders.

To automate this detection, Convolutional Neural Networks (CNNs) are used to analyze images of skin, eyes, lips, tongue, and nails. The CNN model—based on AlexNet—learns patterns related to deficiencies in vitamins A, B, C, D, and E. After training, the system can classify new images and predict deficiencies quickly and accurately, offering an alternative to expensive and invasive blood tests.

The literature survey shows strong global interest in applying ML and DL methods for vitamin deficiency detection. Various researchers have used algorithms such as CNN, SVM, KNN, logistic regression, neural networks, fuzzy logic, and image-processing techniques to diagnose deficiencies, recommend diets, and detect skin abnormalities.

The methodology includes steps such as image uploading, cropping, cluster formation, feature extraction, and classification. The system then provides the predicted deficiency along with dietary suggestions. The project outputs annotated images showing detected deficiencies, helping users understand their nutritional health.

Results show that CNN-based models perform highly accurately in identifying vitamin deficiency patterns through skin image analysis. This approach can support healthcare professionals with early detection and timely treatment.

In the future, CNN-powered systems can be integrated with mobile devices for easy at-home screening, making vitamin deficiency detection faster, more accessible, and more reliable.

Conclusion

Employing a systematic approach to detect deficiencies in vitamins A, B, C, D, and E is crucial for maintaining overall health. By monitoring these essential vitamins, potential imbalances or deficiencies can be identified early, allowing for timely intervention and correction. This proactive strategy acts as a preventive measure, ensuring that individuals receive adequate nutritional support to sustain vital bodily functions. The incorporation of reliable detection methods contributes to a holistic healthcare approach, emphasizing the importance of maintaining balanced vitamin levels for optimal well-being.

References

[1] S.Khare,“IdentificationOfVitaminDDeficiency,”NationalLibraryOfMedicineJournal,vol.05,no.03,pp.3456-3465,2023. [2] D.A. Abhuhani, “DetectingVitaminADeficiencyUsingMachine Learning,”IEEEPublisher,vol.05,no.10,p.709,2023. [3] K.V.Satyanarayana,“IdentificationOfVitaminDeficiencyAndRecommendationOfRichVitaminFoodUsingMachineLearning,”JournalOfSurveyInFisheriesSciences,vol.10,no.02,pp.2766-2777,2023. [4] R. Moholkar, “Vitamin Deficiency Detection Using Image Processing and Neural Network,” International Journal Of Advance Research And Innovative IdeasInEducation,vol.09,no.03,pp.4175-4183,2023 [5] E. K,“DiagnosisOfVitaminDeficiencyInHumanBeingsUsingDNNAlgorithm,” IEEEpublisher,vol.10,no.03,pp.1627-1632,2023. [6] D. Dandavate, “Vitamin Deficiency Detection Using Image Processing and Artificial Intelligence,” International Research Journal Of Engineering AndTechnology,vol.08,no.04,pp.3421-3424,2021. [7] H.Tamune,“EfficientPredictionOfVitaminBDeficienciesUsingMachineLearning,”Frontiers,vol.10,no.06,pp.1-9,2020. [8] M.D.R,“ReviewPaperWritingforVitaminDeficiencyDetection,”JournalOfEmergingTechnologiesAndInnovativeResearch,vol.10,no.03,pp.386-392,2023. [9] Ms.A.Bhavana,“VitaminDeficiencyAndFoodRecommendationSystemUsingMachineLearning,”JournalOfEmergingTechnologiesAndInnovativeResearch,vol.09,no.05,pp.413-417,2022 [10] J.J.Knapik,“Clinically DiagnosedVitaminDeficienciesAnd Disorder In EntireUnitedStateMilitaryPopulation,”NutritionJournal,vol.08,no. 03,pp.601-609,2021.

Copyright

Copyright © 2025 Mr. CTM Praveen Kumar, G Mohammed Inzamam Ul Khalandri. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.