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Classification of Illness

Classification of Illness

The meaning of illness is individualized to each person who experiences an alteration in health. In contrast, disease is a medical term indicating a pathological change in the structure and function of the body or mind. It is a condition that has specific symptoms and boundaries.
An illness is a response to a disease in which the person's level of functioning is changed compared with a previous level. The general classification of illness includes acute and chronic.
Acute illness is severe...

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Related Experiment Video

Updated: Oct 16, 2025

Automated Measurement of Cryptococcal Species Polysaccharide Capsule and Cell Body

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Published on: January 11, 2018

COVID-19 Diagnosis Using Capsule Network and Fuzzy C-Means and Mayfly Optimization Algorithm.

Ali Farki¹, Zahra Salekshahrezaee², Arash Mohammadi Tofigh³

¹Department of Information Technology Engineering, Industrial and Systems Engineering Faculty, Tarbiat Modares University, Tehran, Iran.

Biomed Research International

|October 22, 2021

Summary

A novel computer-aided method using fuzzy C-ordered means (FCOM) and an enhanced capsule network (ECN) optimized by the mayfly optimization (MFO) algorithm accurately diagnoses COVID-19 from chest X-rays.

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Area of Science:

Medical Imaging and Artificial Intelligence
Computational Pathology
Infectious Disease Diagnostics

Background:

Early and accurate diagnosis of COVID-19 is critical for effective treatment and control.
Computer-aided diagnostic tools can enhance the accuracy and efficiency of disease detection.
Chest X-ray imaging is a widely accessible modality for assessing pulmonary conditions, including COVID-19.

Purpose of the Study:

To propose an optimal computer-aided method for the accurate diagnosis of COVID-19 using chest X-ray images.
To develop an enhanced capsule network (ECN) integrated with fuzzy C-ordered means (FCOM) and optimized by the mayfly optimization (MFO) algorithm.
To evaluate the performance of the proposed method against existing state-of-the-art techniques.

Main Methods:

Implementation of a hybrid approach combining fuzzy C-ordered means (FCOM) and an enhanced capsule network (ECN).
Optimization of the enhanced capsule network (ECN) using the mayfly optimization (MFO) algorithm.
Validation of the proposed method on publicly available chest X-ray datasets for COVID-19 diagnosis.

Main Results:

The proposed FCOM-MFO-ECN method achieved a high accuracy of 97.08% and precision of 97.29%.
The method demonstrated superior performance compared to established methods like FOMPA, MID, and 4S-DT.
Sensitivity reached 97.1%, and the F1-score was 97.47%, indicating excellent diagnostic capability.

Conclusions:

The developed fuzzy C-ordered means (FCOM) and mayfly optimization (MFO)-enhanced capsule network (ECN) offers a highly accurate and reliable approach for COVID-19 diagnosis from chest X-rays.
This computer-aided method shows significant potential for improving diagnostic accuracy in clinical settings.
The study highlights the effectiveness of integrating advanced optimization algorithms with deep learning models for medical image analysis.