Diagnosis of Vitamin Deficiency in Human Beings using DNN Algorithm

Abstract

The proposed RCNN-based classification system for vitamin deficiency in skin surface microscopy images involves several important steps. The first step is to extract relevant features from the images, which in this case will be border/edge information obtained through the use of Blur Trace (BT) techniques. The BT analysis is a powerful tool for extracting meaningful information from images, and it has been shown to be effective in pattern recognition tasks similar to the one being proposed here. The next step in the process is to perform preprocessing on the images to remove unwanted elements such as hair and noise. This is achieved through the use of nonlinear filtering, specifically median filtering, which has been chosen for its superior performance compared to linear filtering methods. The filtered images are then analyzed to extract energy characteristics that are used to accurately categorize the patterns of vitamin deficiency present in the images. The final stage of the system is the classification of the dermoscopy image into one of the predefined categories, such as Normal, Benign, or Malignant. This is accomplished through the use of the RCNN, which has been trained on the features extracted from the images. The RCNN is a highly advanced machine learning algorithm that has been shown to perform well in a wide range of pattern recognition tasks, making it an ideal choice for this application. The ultimate goal of this research is to contribute to the field of dermatology by improving the accuracy of diagnosing vitamin deficiency and enhancing therapy efficacy through the use of cutting-edge imaging technology. By combining the power of the RCNN with the capabilities of the BT analysis, it is expected that a highly accurate and effective classification system will be developed, which will benefit patients and healthcare practitioners alike.