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An Alternative Diagnostic Method for C. neoformans: Preliminary Results of Deep-Learning Based Detection Model
Ayse Seyer Cagatan1, Mubarak Taiwo Mustapha2, Cemile Bagkur3
1Department of Medical and Clinical Microbiology, Faculty of Medicine, Cyprus International University, TRNC Mersin 10, Nicosia 99010, Turkey.
Abstract:
Cryptococcus neoformans is an opportunistic fungal pathogen with significant medical importance, especially in immunosuppressed patients. It is the causative agent of cryptococcosis. An estimated 220,000 annual cases of cryptococcal meningitis (CM) occur among people with HIV/AIDS globally, resulting in nearly 181,000 deaths. The gold standards for the diagnosis are either direct microscopic identification or fungal cultures. However, these diagnostic methods need special types of equipment and clinical expertise, and relatively low sensitivities have also been reported. This study aims to produce and implement a deep-learning approach to detect C. neoformans in patient samples. Therefore, we adopted the state-of-the-art VGG16 model, which determines the output information from a single image. Images that contain C. neoformans are designated positive, while others are designated negative throughout this section. Model training, validation, testing, and evaluation were conducted using frameworks and libraries. The state-of-the-art VGG16 model produced an accuracy and loss of 86.88% and 0.36203, respectively. Results prove that the deep learning framework VGG16 can be helpful as an alternative diagnostic method for the rapid and accurate identification of the C. neoformans, leading to early diagnosis and subsequent treatment. Further studies should include more and higher quality images to eliminate the limitations of the adopted deep learning model.
Insights
A deep learning model, VGG16, accurately detects Cryptococcus neoformans, an important fungal pathogen. This AI approach offers a promising alternative for rapid cryptococcosis diagnosis in clinical settings.
Area of Science:
- Medical Mycology
- Computational Biology
- Infectious Diseases
Background:
- Cryptococcus neoformans is an opportunistic fungal pathogen causing cryptococcosis, particularly cryptococcal meningitis (CM) in immunocompromised individuals.
- Global estimates indicate over 220,000 annual CM cases in people with HIV/AIDS, leading to nearly 181,000 deaths.
- Current diagnostic methods like microscopy and culture require specialized equipment and expertise, with reported limitations in sensitivity.
Purpose of the Study:
- To develop and implement a deep learning approach for the detection of Cryptococcus neoformans in patient samples.
- To evaluate the efficacy of the VGG16 model for identifying C. neoformans from images.
Main Methods:
- The study utilized the VGG16 deep learning model, a state-of-the-art architecture for image analysis.
- A dataset of images was curated, with positive images containing C. neoformans and negative images not containing the fungus.
- Model training, validation, testing, and evaluation were performed using established machine learning frameworks.
Main Results:
- The VGG16 model achieved an accuracy of 86.88% in detecting C. neoformans.
- The model demonstrated a loss value of 0.36203, indicating effective learning during training.
Conclusions:
- The deep learning framework VGG16 shows potential as an alternative diagnostic tool for rapid and accurate identification of C. neoformans.
- This AI-driven method could facilitate earlier diagnosis and treatment of cryptococcosis.
- Future research should focus on enhancing the model's performance by incorporating larger and higher-quality image datasets.
