Published Paper Details:

In this work, we introduce an advanced intelligent system designed to use deep learning techniques to identify and distinguish between human tissue status and vitamin defi- ciencies effectively. Our approach begins with the use of image aggregation techniques to isolate and better focus lesion areas in images. The large amount of data available on the image databases is used to fine-tune the pre-trained networks to obtain color, texture, and structure features related to the pathologies. This approach is effective and provides an oppor- tunity to detect vitamin deficiencies quickly and unequivocally even to expose patients to invasive procedures. The system aims to conduct an image analysis of physical characteris- tics associated with deficiency diseases, which include pale skin, smooth tongue, and dry eyes. Our primary objectives are to thoroughly evaluate the effectiveness of the proposed segmentation procedure, determine which aspects are most critical for exact classification, and compare the effectiveness of our grouping results with those achieved using alternative methods. This method aims to enhance the precision and reliability of identifying and distinguishing between various tissue types and deficiencies, thereby advancing diagnostic capabilities in medical imaging significantly.