Detection of acute lymphocyte leukemia using k-nearest neighbor algorithm based on shape and histogram features

Abstract

Abstract. Leukemia is a type of cancer which is caused by malignant neoplasms in leukocyte cells. Leukemia disease which can cause death quickly enough for the sufferer is a type of acute lymphocyte leukemia (ALL). In this study, we propose automatic detection of lymphocyte leukemia through classification of lymphocyte cell images obtained from peripheral blood smear single cell. There are two main objectives in this study. The first is to extract featuring cells. The second objective is to classify the lymphocyte cells into two classes, namely normal and abnormal lymphocytes. In conducting this study, we use combination of shape feature and histogram feature, and the classification algorithm is knearest Neighbour with k variation is 1, 3, 5, 7, 9, 11, 13, and 15. The best level of accuracy, sensitivity, and specificity in this study are 90%, 90%, and 90%, and they were obtained from combined features of area-perimeter-mean-standard deviation with k=7.

Automated Analysis of Blood Smear Images for Leukemia Detection: A Comprehensive Review

“…In their empirical study, they determined that k = 4 performed the best classiication. Purwanti et al [184] tried all the odd values of k from 1 to 15 and concluded that k = 7 gives the optimal results for the given dataset. Bhattacharjee et al [93] used the value of k as 1 to perform the classiication using low computational resources.…”

A Review on Traditional Machine Learning and Deep Learning Models for WBCs Classification in Blood Smear Images

“…6. Many conventional supervised learning methods have been used to classify leucocytes in microscopic blood smear images, such as Support Vector Machine (SVM) [32][33][34], Naive Bayes (NB) [35][36][37], K-Nearest Neighbor (KNN) [38][39][40], and Artificial Neural Network (ANN) [41][42][43]. Some popular WBCs nuclei detection techniques are identified and reviewed, which are presented in Table 2.…”

Leukemia Diagnosis using Machine Learning Classifiers Based on Correlation Attribute Eval Feature Selection

“…Purwanti et. al., [21] propose an automated method for detecting lymphocyte leukemia by classifying single lymphocyte images obtained from peripheral blood smears. This study has two primary objectives.…”