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A Novel Deep Learning-Based Black Fungus Disease Identification Using Modified Hybrid Learning Methodology.

S Karthikeyan1, G Ramkumar2, S Aravindkumar3

  • 1Department of Electronics and Communication Engineering, Sathyabama Institute of Science and Technology, Chennai, Tamil Nadu, India.

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Summary
This summary is machine-generated.

A novel artificial intelligence (AI) strategy, the hybrid learning-based neural network classifier (HLNNC), effectively detects black fungus (mucormycosis) in patients with COVID-19 using eye images. This AI approach aids in early diagnosis of this rare but severe fungal infection.

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Area of Science:

  • Medical Artificial Intelligence
  • Infectious Diseases
  • Ophthalmology

Background:

  • COVID-19 pandemic has led to increased incidence of opportunistic infections, including mucormycosis (black fungus).
  • Mucormycosis, a rare but devastating fungal infection, disproportionately affects immunocompromised individuals, particularly those with COVID-19.
  • Early and accurate diagnosis of black fungus is crucial for effective treatment and improved patient outcomes.

Purpose of the Study:

  • To develop and evaluate a novel artificial intelligence (AI) strategy for detecting black fungus infection.
  • To design a hybrid learning-based neural network classifier (HLNNC) integrating Convolutional Neural Network (CNN) and Support Vector Machine (SVM) principles.
  • To utilize a dataset of eye photographs from patients with and without black fungus for AI model training and testing.

Main Methods:

  • Implementation of a hybrid learning-based neural network classifier (HLNNC) for AI-driven diagnosis.
  • Application of image processing techniques including image acquisition, preprocessing, feature extraction, and classification.
  • Training and testing the HLNNC model on a curated dataset of eye images from COVID-19 patients with and without black fungus.

Main Results:

  • The proposed HLNNC scheme demonstrated efficacy in identifying black fungus infection from eye photographs.
  • Performance analysis confirmed the accuracy and reliability of the AI-based diagnostic approach.
  • Graphical representation of results provided clear specifications of the model's performance.

Conclusions:

  • The developed HLNNC model offers a promising AI-powered tool for the early detection of black fungus in COVID-19 patients.
  • This AI strategy can aid clinicians in diagnosing mucormycosis, potentially reducing mortality and morbidity.
  • Further research and validation are warranted to integrate this AI tool into clinical practice for managing post-COVID complications.