Colorectal Histology CSV Multi-classification Accuracy Comparison using Various Machine Learning Models

被引：0

作者：

Rizalputri, Lavita Nuraviana ^{[1
]}

Pranata, Timothy ^{[1
]}

Tanjung, Nancy Silvia ^{[1
]}

Auliya, Hasna Marhamah ^{[1
]}

Harimurti, Suksmandhira ^{[1
]}

Anshori, Isa ^{[1
]}

机构：

[1] Bandung Inst Technol, Biomed Engn, Bandung, Indonesia

来源：

PROCEEDING OF 2019 INTERNATIONAL CONFERENCE ON ELECTRICAL ENGINEERING AND INFORMATICS (ICEEI) | 2019年

关键词：

Convolutional Neural Network; K-Nearest Neighbour; Logistic Regression; Random Forest; Colorectal Histology;

D O I：

10.1109/iceei47359.2019.8988846

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

The MNIST database was derived from a larger dataset known as the NIST Special Database. In this paper, colorectal histology CSV data is multi-classified with several method: Convolutional Neural Network (CNN), K-Nearest Neighbour (KNN), Logistic Regression, and Random Forest. The aim of this paper is to compare the performance of each method and define the optimum algorithm; the best method is obtained with CNN with 82.2% accuracy.

引用

页码：58 / 62

页数：5

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