Empirical Evaluation of Learning assisted Vitamin Deficiency Detection Methodology using BioMedical Image Processing Logics

Vitamin deficiencies manifest in diverse clinical symptoms that often require early detection to prevent long-term health complications. This research presents a novel learning-assisted methodology for detecting vitamin deficiencies through advanced biomedical image processing. The system integrates comprehensive data acquisition, rigorous pre-processing, robust feature extraction, and state-of-the-art machine learning techniques. Biomedical images, including retinal scans, skin images, and oral cavity photographs, are meticulously curated and pre-processed using Gaussian filtering, normalization, and contrast enhancement. These steps ensure that critical diagnostic features are preserved, with quantitative metrics such as PSNR and SSIM confirming enhanced image quality. Feature extraction is achieved through methods like Canny edge detection and Local Binary Patterns, which capture subtle anatomical details. The core of the methodology employs Convolutional Neural Networks (CNNs) and leverages transfer learning with a fine-tuned ResNet model. An ensemble model further combines the strengths of multiple architectures, achieving an impressive accuracy of 92.0%, alongside high precision, recall, and F 1 scores. Empirical evaluations indicate that the proposed framework not only outperforms baseline models'such as a standard CNN model with 85.2% accuracy-but also exhibits robustness across diverse imaging modalities. This work underscores the potential of integrating deep learning and image processing techniques to offer a reliable, automated tool for early vitamin deficiency detection. The high accuracy and strong performance metrics demonstrate the system—s clinical relevance and promise as a decision-support tool, paving the way for further advancements in automated biomedical diagnostics.