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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: Nov 19, 2025

Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections

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Published on: September 19, 2025

A Machine Learning Approach to Diagnosing Lung and Colon Cancer Using a Deep Learning-Based Classification Framework.

Mehedi Masud¹, Niloy Sikder², Abdullah-Al Nahid³

¹Department of Computer Science, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia.

Sensors (Basel, Switzerland)

|January 27, 2021

Summary

Artificial Intelligence (AI) and Deep Learning (DL) can automate cancer diagnosis. A new framework accurately identifies lung and colon cancer tissues from histopathological images, improving early detection.

Keywords:

colon cancer detection deep learning histopathological image analysis image classification lung cancer detection

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Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

Area of Science:

Oncology
Medical Imaging
Artificial Intelligence in Healthcare

Background:

Cancer remains a leading global cause of death, with lung and colon cancers being particularly prevalent and deadly.
Early cancer detection significantly improves patient survival rates.
Traditional diagnostic methods can be time-consuming and costly.

Purpose of the Study:

To develop an automated classification framework for differentiating benign and malignant lung and colon tissues.
To leverage Artificial Intelligence (AI), Deep Learning (DL), and Digital Image Processing (DIP) for enhanced cancer diagnosis.
To improve the speed and cost-effectiveness of cancer identification.

Main Methods:

Utilized Deep Learning (DL) and Digital Image Processing (DIP) techniques.
Developed a classification framework to analyze histopathological images of lung and colon tissues.
Trained the model to distinguish between two benign and three malignant tissue types.

Main Results:

The proposed framework achieved a maximum accuracy of 96.33% in identifying cancerous tissues.
Demonstrated the potential of AI and DL in automating the analysis of histopathological images.
Successfully differentiated between various types of lung and colon cancer tissues.

Conclusions:

The developed AI-powered framework offers a reliable and automatic solution for lung and colon cancer identification.
Implementation can assist medical professionals in faster and more accurate cancer diagnosis.
This approach holds promise for improving patient outcomes through early and precise detection.