Improving Burn Diagnosis in Medical Image Retrieval from Grafting Burn Samples Using B-Coefficients and the Clahe Algorithm

This study focuses on the vital difficulty of burn assessment in medical image retrieval from grafted burn specimens particularly in resource-constrained contexts where speedy and precise diagnoses are required. Our solution combines sophisticated machine learning techniques, namely an Artificial Neural Network (ANN), with the Contrast Limited Adaptive Histogram Equalisation (CLAHE) algorithm in an Image Reclamation system. The statistical assessments of kurtosis value (KCLAHE=144.83) compared to the query image (Kquery=131.17) indicate a distribution with more pronounced tails in the CLAHE image, enhancing specific image features. Additionally, increased skewness in the CLAHE image (SCLAHE=5.92) suggests a shift toward higher intensity levels compared to the query image (Squery=4.47), further enhancing discernible image features.Through this incorporation, we carefully retain picture boundaries, boost local contrast, and minimize noise, hence enhancing burn diagnostic accuracy. Statistical analyses, such as kurtosis and skewness analysis, verify the improvements in visible picture aspects, offering significant insights into fundamental texture properties. We increase picture retrieval efficiency by using Bhattacharya coefficients and unique bin analysis, resulting in substantial enhancements in the retrieving score of matched images The ANN successfully differentiates between photos that require grafts and those that do not, providing a speedy and accurate diagnosis for acute burn injuries. This comprehensive technique greatly improves burn diagnosis, especially during emergencies, and shows promise for improving medical procedures. Our study helps to raise patient care standards in difficult medical situations by combining automated evaluation tools, powerful methods for image processing, and machine learning.