Robust Deep Learning Model for Black Fungus Detection Based on Gabor Filter and Transfer Learning

Abstract

Black fungus is a rare and dangerous mycology that usually affects the brain and lungs and could be life-threatening in diabetic cases. Recently, some COVID-19 survivors, especially those with co-morbid diseases, have been susceptible to black fungus. Therefore, recovered COVID-19 patients should seek medical support when they notice mucormycosis symptoms. This paper proposes a novel ensemble deep-learning model that includes three pre-trained models: reset (50), VGG (19), and Inception. Our approach is medically intuitive and efficient compared to the traditional deep learning models. An image dataset was aggregated from various resources and divided into two classes: a black fungus class and a skin infection class. To the best of our knowledge, our study is the first that is concerned with building black fungus detection models based on deep learning algorithms. The proposed approach can significantly improve the performance of the classification task and increase the generalization ability of such a binary classification task. According to the reported results, it has empirically achieved a sensitivity value of 0.9907, a specificity value of 0.9938, a precision value of 0.9938, and a negative predictive value of 0.9907.