Pixel-Wise Classification in Hippocampus Histological Images

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.

Pixel-wise classification in graphene-detection with tree-based machine learning algorithms

“…Especially, image classification using deep learning (DL) has emerged as a game changer technique that has allowed drastic reduction of analysis time from hours to seconds. For example, the convolutional neural network (CNN) has been used in biomedical fields, such as abdominal CT scan, cell, hippocampus, and pancreas segmentations [29][30][31][32], and in analyzing big image data obtained from satellites [33,34] providing significant aid to error-prone human eyes [35][36][37][38].…”

Pixel-wise classification in graphene-detection with tree-based machine learning algorithms

“…Especially, image classification using deep learning (DL) has emerged as a game changer technique * gjtbgf@g.uos.ac.kr that has allowed drastic reduction of analysis time from hours to seconds. For example, the convolutional neural network (CNN) has been used in biomedical fields, such as abdominal CT scan, cell, hippocampus, and pancreas segmentations [29][30][31], and in analyzing big image data obtained from satellites [32,33] providing significant aid to error-prone human eyes.…”