Publication

Abstract : Mucormycosis, one of the significant fungal illnesses caused by the fungus Mucormycetes, is a rare and fatal disease. It is found all over the environment. People with weakened immune systems are more prone to get sick rapidly, including those with diabetes or COVID-19 history. Mucormycosis can affect the eyes and quickly spread to the brain if it enters through the nose, sinuses, or lungs. Existing research in the medical domain shows that deep learning techniques are a promising solution for assisting medical practitioners in making quick decisions. This study aims to use transfer learning with a predefined VGG16 model to create a classifier that can distinguish between mild and severe Mucormycosis illness symptoms. The neural network is trained, validated, and tested by loading black fungus dataset. The accuracy is calculated by varying the number of images. The results show that the proposed model gives an accuracy of 92% for 520 input images.