Published Paper Details:

In many areas of human life, technology has increased efficiency. To understand more about the patient's condition, the medical field has also used the equipment. Many academics are focused on medical image diagnosis in order to advance the health sector. The AISCD (Artificial Immune System Based COVID Detection) model is presented in this study. The histogram feature from the image has been extracted by AISCD, and to speed up model learning, the image training set has been blocked. By virtue of its cluster centre, each block is represented. Therefore, those affecting cluster centres are found by artificial immune genetic algorithm. In the training phase, an error back propagation model was used. Images from datasets with and without COVID infections were used in the experiment. Results and current detection were compared. Results were contrasted with already available detection models, and it was discovered that AISCD had enhanced work performance on a number of evaluating factors.