Micronutrient Deficiency Using Image Processing and Neural Network

Abstract

Vitamin and micronutrient deficiencies are a significant global health concern, leading to various adverse health consequences. Early detection and intervention are essential in addressing these issues. The project introduces an intelligent system that utilizes advanced deep learning techniques to identify and differentiate vitamin deficiencies in human tissue through image analysis. The approach involves an initial step of image clustering to separate and isolate problem areas from the input images. The goal is to evaluate the productivity of image segmentation methods, extract relevant characteristics, and compare classification results with other methods. To accomplish the objectives, a diverse dataset of facial images is gathered and pre-processed, focusing on individuals both with and without visible signs of vitamin deficiencies Following this, a CNN algorithm, inspired by models like Alex Net, is created and trained using the pre-processed dataset. The CNN is used to identify and classify features based on different types of vitamin deficiencies, enabling an automated and accurate assessment based on facial images.

Introduction

I. INTRODUCTION

Micronutrient deficiency detection is the process of identifying insufficient levels of essential vitamins in the human body. These nutrients plays a important role in our health, influencing functions like energy production, immune response, and overall well-being. Identifying micronutrient deficiencies early can help prevent health issues and promote a balanced lifestyle.

The skin is a dense organ, and its health is directly tied to overall health and wellness. Thus, proper attention should be paid to skin health and the early detection of skin disorders and diseases, including skin cancers. By doing so, individuals can take proactive measures to maintain their skin health and improve their chances of avoiding serious conditions such as skin diseases.

The goal is to acknowledge the patterns and characteristics associated with the deficiency and to categorize the images according to the information.The medical and health sciences fields have shown growing interest in employing Machine Learning (ML) and Deep Learning (DL) techniques to enhance clinical decision making.

The CNN algorithm is trained on this dataset, learning to recognize distinctive features and patterns that differentiate between normal and deficient states. Once trained, the CNN is capable of accurately classifying new images and identifying potential micronutrient deficiencies based on learned patterns. This automated approach provides a rapid and objective means of detection.

Micronutrients, like vitamin A, B, C, D, and E, each of these vitamins plays a unique role in supporting different perspective of well-being.

Vitamin A enhances our immune system, defending our body against illnesses and supporting our eyes for better vision in low light.

Vitamin B can result in various health issues through supplementation or dietary changes can bring advantages, such as improved energy levels.

Vitamin C protects us from getting sick, and it also makes sure our skin, blood vessels, and bones stay strong.

Vitamin D helps human body, making sure it absorbs calcium, which is important for keeping bones and teeth strong.

Vitamin E is like a shield for our cells, acting as an antioxidant that protects them from damage. It also supports a healthy immune system.

II. LITERATURE SURVEY

1. Authors: - Diaa Adden Abuhani, Jowaria Khan, Hana Sulieman.

Vitamin A deficiency is usually detected through clinical assessments of eye signs or biochemically determined by measuring the concentrations . The medical and health sciences fields have shown growing interest in employing Machine Learning (ML) and Deep Learning (DL) techniques to enhance clinical decision making. To tackle this problem, researchers investigated. using machine learning (ML) techniques on existing health records to detect Vitamin A deficiency in children.

2. Authors:- K.V. Satyanarayana, Gangireddy Pujitha, Battula vishal, Indukuru pranay varma.

Two technological advancements for healthy eating habits: a personalized diet recommendation system and a vitamin deficiency detection and recommendation project. The first system analyses user data to suggest customized meal plans, while the second project focuses on identifying deficiencies and recommending nutrient-rich foods.

3. Authors: - Rutuja Moholkar, Mansi Kamble, Gauri Bobade, Sai Jyoti Shinde.

Develop an Android Application and cost-free desktop app that uses images to detect vitamin deficiencies instead of expensive and inconvenient blood tests. Users upload images of specific body parts like tongue and nails, and image processing techniques are used to analyse and extract features, Neural Network and Fuzzy Membership Function and Defuzzification. study focuses on automated facial skin disease detection using algorithms. This approach aims for a faster and easier alternative to traditional blood tests.

4. Authors: - Elavarasi K, Shanmugapriya K.

Support Vector Machine and Blur Trace (BT) techniques Region-based Convolutional Neural Network. diagnosing vitamin deficiency by above algorithms for skin images. this will help us to Early detection of skin conditions, like Normal, Benign, or Malignant. this is crucial for improving survival rates, emphasizing the significance of skin health.

5. Authors: - Dr. Arati Dandavate, Priyanka Gore, Namita Naikwadi, Shrushti Sable, Muskan Tilwani.

By introducing AI System to diagnosis of vitamin deficiency at early stage of deficiency. The application is trained to distinguish between normal people photos of eyes, lips, tongue, and nails with user photos and people having vitamin deficiency.

6. Authors: - Mrs Shivani Devakar, Dr. Sachin Bere S.

It consists of image acquisition, preprocessing, and segmentation, alongside feature extraction and classification. Analysing symptoms and their correlations is essential for accurate detection. Functional requirements include implementing video surveillance with automated convolutional neural network technology. The use of CNN algorithm also played a very crucial role in the entire programming and evaluation of the current situations of the neurons

III. METHODOLOGY

To detect vitamin deficiencies using Convolutional Neural Networks (CNN), we begin by collecting different medical images that show symptoms of various deficiencies. We clean and organize these images carefully. Then project uses the AlexNet model for image classification, we create a smart computer model (CNN) that learns from these pictures to recognize patterns connected to specific vitamin issues.

1. Input the Image: - Start by uploading or inputting the image containing physical symptoms potentially indicative of micronutrient deficiencies into the system.
2. Image Cropping: -Utilize techniques to crop the input image, focusing on isolating specific regions or areas that exhibit notable symptoms related to vitamin deficiencies.
3. Formation of Clustered Image:-Group similar regions or clusters within the cropped image based on visual similarities, allowing for more targeted analysis and feature extraction.
4. Feature Extraction: - Feature extraction involves identifying and isolating important characteristics from clustered images, such as texture, colour, shape, and intensity variations. These extracted features are essential for recognizing distinct patterns linked to particular nutrient deficiencies.
5. Classification: -Utilize classification algorithms like convolutional neural networks or other machine learning models to evaluate the extracted features. This process categorizes the clustered images into various deficiency classes, aiding precise diagnosis and intervention strategies.
6. Predict deficiency: - The result display presents the detected deficiency and its corresponding description to the user. This information is generated based on the analysis of the uploaded image using the neural network model. Users receive concise feedback regarding the identified deficiency.

                                                                                 Fig 1 (A) methodology process

The project output consists of annotated images depicting detected vitamin deficiencies in individuals, utilizing image processing and convolutional neural network. Each deficiency is clearly labelled, providing visual feedback for personal diagnosis. Furthermore, alongside the deficiency annotations, dietary intervention suggestions are included, showcasing specific foods known to address each deficiency. This personalized approach aims to enhance awareness and facilitate informed decision-making regarding individual nutritional health, empowering users to take proactive steps towards improving their well-being.

IV. RESULTS AND DISCUSSIONS

The utilization of Convolutional Neural Network (CNN) models, drawing inspiration from architectures like Alex Net, has advanced the field of Micronutrient deficiency detection through skin image analysis. With an impressive accuracy, this methodology showcases significant advancements in identifying deficiencies in vitamin A, B, C, D, and E. This method is really good at spotting signs of vitamin deficiencies. It works so well because it uses smart techniques to break down pictures of affected skin and find specific areas that might show signs of lacking certain vitamins. This helps make the method work better for everyone. It helps doctors figure out what's wrong and how to assist individuals who might not have enough vitamins. So, using this technology to look at affected skin areas might help doctors to determine if someone has vitamin problems early on, which implies they can obtain help more. This could be a big deal in healthcare because it could change how we find and treat vitamin deficiencies.

V. FUTURE SCOPE

The future scope for detecting vitamin deficiencies using CNN (Convolutional Neural Networks) involves leveraging this technology to analyse medical images like skin scans. CNNs excel at pattern recognition, making them eligible for identifying subtle signs of deficiency. This approach could lead to more precise and quicker diagnosis through automated systems, improving efficiency in healthcare. Additionally, integrating CNNs with portable devices could enable easy and accessible at-home screenings, enhancing the overall management of vitamin deficiencies.

Conclusion

Employing a systematic approach for detecting vitamin deficiencies in vitamin 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 serves as a preventive measure, ensuring that individuals receive adequate nutritional support to support various bodily functions. The incorporation of reliable detection methods contributes to a holistic approach to healthcare, emphasizing the significance of balanced vitamin levels for optimal well-being.

References

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Copyright

Copyright © 2024 Mr. Hari Krishna , Asma Begum, Manjunath B, Shaheen Begum, Saniya Tahasin T. 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.