Acute Lymphoblastic Leukemia Disease Detection Using Image Processing and Machine Learning

被引：0

作者：

Chavan, Abhishek D. ^{[1
]}

Thakre, Anuradha ^{[1
]}

Chopade, Tulsi Vijay ^{[1
]}

Fernandes, Jessica ^{[1
]}

Gawari, Omkar S. ^{[1
]}

Gore, Sonal ^{[1
]}

机构：

[1] Pimpri Chinchwad Coll Engn, Dept Comp Engn, Pune, India

来源：

ADVANCES IN COMPUTING AND DATA SCIENCES (ICACDS 2022), PT II | 2022年 / 1614卷

关键词：

Image acquisition; Image pre-processing; Image segmentation; Feature extraction; Machine learning; Classification; Artificial neural networks; Support vector machine; Convolutional neural network; Logistic regression; Naive Bayes; K-nearest neighbors; Decision tree; Random Forest algorithms;

D O I：

10.1007/978-3-031-12641-3_4

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Acute Lymphoblastic Leukemia (ALL) is a cancer type in which there is an increase of white blood cells (WBCs) in our body. This article presents a method that detects the presence of these abnormal cells in the bloodstream using machine learning and image processing algorithms. A methodology to identify ALL using machine learning classification techniques like Convolutional Neural Network (CNN), Artificial Neural Network (ANN), Logistic Regression, and Support Vector Machine (SVM) using the existing dataset (ALL-IDB2) is discussed. The outcome of the paper is to analyze the ALL IDB2 dataset and predict the output as ALL infected or not. According to the experimental results, it is observed that the performance of CNN supersites other machine learning classifiers for the proposed classification in terms of accuracy.

引用

页码：38 / 51

页数：14

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