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Classification of Systems-II

Classification of Systems-II

Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,

Classification of Systems-I

Classification of Systems-I

Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
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Related Experiment Video

Updated: Sep 18, 2025

Combining Reflectance Confocal Microscopy with Optical Coherence Tomography for Noninvasive Diagnosis of Skin Cancers via Image Acquisition

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Two-stage ensemble learning framework for automated classification of keratoconus severity.

Zahra J Muhsin¹, Rami Qahwaji¹, Ibrahim Ghafir¹

¹Faculty of Engineering and Digital Technologies, University of Bradford, Bradford, UK.

Computers in Biology and Medicine

|June 26, 2025

Summary

This study introduces an advanced two-stage ensemble learning model for automated keratoconus (KC) staging. The model achieves high accuracy in classifying KC severity, aiding in timely patient intervention.

Keywords:

Corneal tomography Ensemble learning Feature selection Keratoconus Severity staging

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Published on: October 21, 2022

Area of Science:

Ophthalmology
Medical Imaging
Machine Learning

Background:

Accurate keratoconus (KC) staging is vital for patient care.
Traditional machine learning (ML) models have limitations in KC staging.
This study proposes an advanced two-stage ensemble learning model for automated KC staging.

Purpose of the Study:

To develop and validate a novel two-stage ensemble learning model for automated KC severity staging.
To improve the accuracy and reliability of KC staging compared to existing methods.
To provide a tool for tracking KC progression and treatment effectiveness.

Main Methods:

Utilized a clinical dataset from Pentacam corneal tomography.
Selected key Pentacam indices strongly correlated with KC severity through rigorous feature selection.
Developed a two-stage ensemble learner combining Random Forest, Gradient Boost, Decision Tree, and Support Vector Machine models.
Employed stacking for three base learners and a meta-classifier for final staging.

Main Results:

The proposed model achieved superior performance with 99.41% validation accuracy, 99.43% precision, and 99.41% sensitivity.
F1 and F2 scores were 99.42% and 99.41%, respectively, with a Matthew's Correlation Coefficient of 0.993.
The model demonstrated exceptional consistency and generalizability, achieving 99% accuracy on unseen test data for stages 0-4.

Conclusions:

The developed model offers a robust foundation for a reliable diagnostic tool for KC severity.
It can aid in detecting KC stages, monitoring disease progression, and evaluating treatment efficacy.
Collaboration with clinicians ensures the model's practical applicability in patient care.