Pixel-Wise Classification in Hippocampus Histological Images

Author:

Vizcaíno Alfonso1ORCID,Sánchez-Cruz Hermilo1ORCID,Sossa Humberto2ORCID,Quintanar J. Luis3ORCID

Affiliation:

1. Departamento de Ciencias de la Computación, Universidad Autónoma de Aguascalientes, Aguascalientes 20131, Mexico

2. Centro de Investigación en Computación, Instituto Politécnico Nacional, Ciudad de México, Mexico

3. Departamento de Fisiología y Farmacología, Universidad Autónoma de Aguascalientes, Aguascalientes 20131, Mexico

Abstract

This paper presents a method for pixel-wise classification applied for the first time on hippocampus histological images. The goal is achieved by representing pixels in a 14-D vector, composed of grey-level information and moment invariants. Then, several popular machine learning models are used to categorize them, and multiple metrics are computed to evaluate the performance of the different models. The multilayer perceptron, random forest, support vector machine, and radial basis function networks were compared, achieving the multilayer perceptron model the highest result on accuracy metric, AUC, and F 1 score with highly satisfactory results for substituting a manual classification task, due to an expert opinion in the hippocampus histological images.

Funder

Instituto Politécnico Nacional

Publisher

Hindawi Limited

Subject

Applied Mathematics,General Immunology and Microbiology,General Biochemistry, Genetics and Molecular Biology,Modeling and Simulation,General Medicine

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