Open Access

ARTICLE

Innovative Fungal Disease Diagnosis System Using Convolutional Neural Network

Tahir Alyas^1,*, Khalid Alissa², Abdul Salam Mohammad³, Shazia Asif⁴, Tauqeer Faiz⁵, Gulzar Ahmed⁶

1 Department of Computer Science, Lahore Garrison University, Lahore, 54000, Pakistan
2 Networks and Communications Department, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam, 31441, Saudi Arabia
3 Department of General Education, Skyline University College, University City of Sharjah, UAE
4 Higher Colleges of Technology, Abu Dhabi, UAE
5 Department of Enterprise Computing, Skyline University College, Sharjah, UAE
6 Department of Computer Science, University of South Asia, Lahore, 54000, Pakistan

* Corresponding Author: Tahir Alyas. Email:

Computers, Materials & Continua 2022, 73(3), 4869-4883. https://doi.org/10.32604/cmc.2022.031376

Received 16 April 2022; Accepted 26 May 2022; Issue published 28 July 2022

Abstract

Fungal disease affects more than a billion people worldwide, resulting in different types of fungus diseases facing life-threatening infections. The outer layer of your body is called the integumentary system. Your skin, hair, nails, and glands are all part of it. These organs and tissues serve as your first line of defence against bacteria while protecting you from harm and the sun. The It serves as a barrier between the outside world and the regulated environment inside our bodies and a regulating effect. Heat, light, damage, and illness are all protected by it. Fungi-caused infections are found in almost every part of the natural world. When an invasive fungus takes over a body region and overwhelms the immune system, it causes fungal infections in people. Another primary goal of this study was to create a Convolutional Neural Network (CNN)-based technique for detecting and classifying various types of fungal diseases. There are numerous fungal illnesses, but only two have been identified and classified using the proposed Innovative Fungal Disease Diagnosis (IFDD) system of Candidiasis and Tinea Infections. This paper aims to detect infected skin issues and provide treatment recommendations based on proposed system findings. To identify and categorize fungal infections, deep machine learning techniques are utilized. A CNN architecture was created, and it produced a promising outcome to improve the proposed system accuracy. The collected findings demonstrated that CNN might be used to identify and classify numerous species of fungal spores early and estimate all conceivable fungus hazards. Our CNN-Based can detect fungal diseases through medical images; earmarked IFDD system has a predictive performance of 99.6% accuracy.

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Keywords

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