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
收藏
页码：58 / 62
页数：5
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