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Hemorrhagic Stroke l: Introduction01:17

Hemorrhagic Stroke l: Introduction

A hemorrhagic stroke is an acute neurological event that occurs when a weakened cerebral blood vessel ruptures, allowing blood to accumulate within or around the brain. The sudden release of blood forms a focal hematoma that increases intracranial pressure, displaces neural tissue, and can obstruct cerebrospinal fluid pathways. These effects may be compounded by intraventricular extension of the hemorrhage, cerebral edema, or compression of adjacent structures, all of which contribute to...

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Brain Hemorrhage Classification in CT Scan Images Using Minimalist Machine Learning.

José-Luis Solorio-Ramírez1, Magdalena Saldana-Perez1, Miltiadis D Lytras2

  • 1Centro de Investigación en Computación, Instituto Politécnico Nacional, CDMX 07700, Mexico.

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|August 27, 2021
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Summary
This summary is machine-generated.

A new explainable artificial intelligence (X-AI) method using Minimalist Machine Learning (MML) and dMeans attribute selection achieved 86.50% accuracy for classifying brain CT scans, outperforming other algorithms in specificity.

Keywords:
eXplainable artificial intelligenceimage classificationmachine learningminimalist machine learning

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Area of Science:

  • Medical Imaging Analysis
  • Artificial Intelligence
  • Machine Learning

Background:

  • Pattern classification algorithms have numerous applications, with Multi-Layer Perceptron (MLP) and Support Vector Machine (SVM) being widely used.
  • Explainable Artificial Intelligence (X-AI) is an emerging trend focused on making machine learning (ML) algorithms more understandable.
  • Existing research has limited focus on X-AI, despite its potential to improve user comprehension of complex models.

Purpose of the Study:

  • To develop a novel pattern classification methodology grounded in the Minimalist Machine Learning (MML) paradigm.
  • To introduce and utilize the dMeans algorithm for higher relevance attribute selection.
  • To evaluate the performance of the new methodology against established classifiers for brain CT image classification.

Main Methods:

  • Implementation of the Minimalist Machine Learning (MML) paradigm combined with the dMeans attribute selection algorithm.
  • Classification of Computed Tomography (CT) brain images (128x128 pixels) into two classes: CT without Hemorrhage and CT with Intra-Ventricular Hemorrhage (IVH).
  • Performance comparison using Leave-One-Out Cross-Validation against MLP, K-Nearest Neighbors (K-NN), Naïve Bayes (NB), SVM, Adaboost, and Random Forest (RF) classifiers.

Main Results:

  • The proposed methodology achieved 86.50% accuracy, matching the best-performing classifier in the study.
  • The methodology demonstrated superior performance in specificity, reaching 91.60%, outperforming all state-of-the-art algorithms.
  • Other tested models generally performed between 50% and 75% accuracy, with sensitivity and specificity ranging from 58% to 86%.

Conclusions:

  • The developed pattern classification methodology offers a highly accurate and explainable approach for brain CT image analysis.
  • The combination of MML and dMeans provides a competitive alternative to traditional classifiers, aligning with X-AI principles.
  • This research contributes to the advancement of understandable AI in medical diagnostics, particularly for identifying Intra-Ventricular Hemorrhage (IVH).