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

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.

A Novel Convolutional Neural Network Model for Malaria Cell Images Classification

Infectious diseases are an imminent danger that faces human beings around the world.Malaria is considered a highly contagious disease.The diagnosis of various diseases,including malaria,was performed manually,but it required a lot of time and had some human errors.Therefore,there is a need to investigate an efficient and fast automatic diagnosis system.Deploying deep learning algorithms can provide a solution in which they can learn complex image patterns and have a rapid improvement in medical image analysis.This study proposed a Convolutional Neural Network(CNN)model to detect malaria automatically.A Malaria Convolutional Neural Network(MCNN)model is proposed in this work to classify the infected cases.MCNN focuses on detecting infected cells,which aids in the computation of parasitemia,or infection measures.The proposed model achieved 0.9929,0.9848,0.9859,0.9924,0.0152,0.0141,0.0071,0.9890,0.9894,and 0.9780 in terms of specificity,sensitivity,precision,accuracy,F1-score,and Matthews Correlation Coefficient,respectively.A comparison was carried out between the proposed model and some recent works in the literature.This comparison demonstrates that the proposed model outperforms the compared works in terms of evaluation metrics.