Md Imran Hasan
ABSTRACT
Black Fungus is a dangerous fungal illness, commonly referred to as ’mucormycosis’, usually infecting uncompromising people. Mucormycosis is generally rare, affecting less than two individuals per million each year, but it is currently 80 times more frequent in India. People of all ages, including prematurity infants, may be impacted. To minimize the time and effort required by medical experts to investigate black fungus disease and enhance the consistency of image identification of black fungus infections, we suggested a transfer learning based CNN model as the dataset for the black fungal disease is limited. The experimental outcomes of several different architectures, including VGG16, InceptionV3, and Xception, are compared and analyzed in this study, and it is observed that Xception has the best performance, with 97.92% training accuracy and 95.60% testing accuracy.
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Index Terms
- Identification of Black Fungus Diseases Using CNN and Transfer-Learning Approach
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