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Mucormycosis, commonly referred to as black fungus, is a dangerous fungal infection that impacts the sinuses, lungs, skin, and brain. It poses a severe threat to immunocompromised individuals, including cancer patients, organ transplant recipients, and people recovering from COVID-19 with co-morbidities. Because timely detection is crucial for reducing patient mortality, computational approaches can assist medical diagnosis. To address this need, the Multi-Class Black Fungus dataset was established, comprising preprocessed clinical images across multiple infection sites. The collection features sixty-six images of eye black fungus, sixty-three and sixty-two images representing mouth infections, and seventy-nine images showing black skin fungus. Utilising this dataset, a pretrained ResNet-50 deep learning model was applied to recognise and classify cases of black fungus, achieving an overall classification accuracy of 96.12 per cent.
Black fungus presents a severe hazard to vulnerable patients, especially those with compromised immune systems following major illnesses or medical interventions. Because rapid medical intervention is necessary to prevent death, developing automated tools to recognise infection signs accurately can support clinical teams in delivering timely care, thereby improving survival rates among high-risk patient groups.
This research could enable computer-aided diagnostic software for clinicians and hospital departments managing immunocompromised patients. The intended users are medical professionals seeking rapid preliminary classification of suspected fungal lesions. Because the model was evaluated on a small, curated set of 270 images, the technology represents early-stage research that requires validation on wider clinical datasets before reaching operational diagnostic deployment.
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Mucormycosis, also known as black fungus, is a rare infection caused by mould that can affect the lungs, brain, skin, and sinuses. People with weakened immune systems due to underlying health conditions (e.g., organ transplant, uncontrolled diabetes, cancer, neutropenia, AIDS, and COVID-19) or drug use are more likely to contract the infection. Recent reports have indicated that a small number of COVID-19 survivors with co-morbidities are particularly vulnerable to black fungus. Therefore, recovered COVID-19 patients should seek medical attention immediately if they experience any mucormycosis symptoms. Patients with COVID-19 have numerous concerns regarding this life-threatening infection, particularly those with immunocompromised states such as solid organ transplant (SOT) and hematopoietic stem cell transplant (HSCT) or tumors, who are more likely to be infected. Early diagnosis and treatment are essential to prevent patient mortality. In this paper, we present the Multi-Class Black Fungus (MCBF) dataset, which consists of three compressed folders containing images. The first folder includes 66 images of eye black fungus after data preprocessing, while the second folder contains 63 images of mouth black fungus after data preprocessing. The third contains 62 images of the mouth after applying data preprocessing, and the last one contains 79 images of black skin fungus after applying data preprocessing. The MCBF dataset contains images that aid in training and validation when using deep learning algorithms to recognise and classify black fungus diseases, then we apply a resnet(50) pretrained model by using MCBF accuracy reaches 96.12%.
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DOI: 10.1109/icetci62771.2024.10704103
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