Accurate automatic detection of acute lymphatic leukemia using a refined simple classification

Abstract

An improved classification technique is presented to identify automatically the acute lymphatic leukemia (ALL) subtypes. An adaptive segmentation procedure is performed on peripheral blood smear images to extract the main features (10 geometric features) from the segmented images of white blood cell (WBC), nucleus, and cytoplasm. To show the importance of the different extracted features for the diagnostic accuracy, a comprehensive study is made on all the possible permutation cases of the features using powerful classifiers which are K‐nearest neighbor (KNN) at different metric functions, support vector machine (SVM) with different kernels, and artificial neural network (ANN). This procedure enables us to construct a feature map depending only on least number of features which lead to the highest diagnostic accuracy. It is found that the features map regarding the vacuoles in the cytoplasm and the regularity of the nucleus membrane gives the highest accurate results. The automatic classification for ALL subtypes based only on these two effective features is assessed using the receiver operating characteristic (ROC) curve and F1‐score measures. It is confirmed that the present technique is highly accurate, and saves the effort and time of training.

Machine Learning in Detection and Classification of Leukemia Using Smear Blood Images: A Systematic Review

“…To design and develop ML algorithms, hematologists have made some of these datasets (that include PBS images) available to researchers. ALL-IDB, one of the most well-known datasets published in two versions, has been utilized in many articles, most of which have diagnosed and classified acute lymphoblastic leukemia (ALL) via different ML techniques [16][17][18][19][20][21]. ere is another published leukemia dataset called Benchmark for the development of ML algorithms, used by some studies.…”

“…Figure 3 displays the diagnostic goals of various types of leukemia based on PBS image processing. In some articles, image analysis has been performed to count the leukocytes [19].…”

“…In this view, the goal of feature extraction is to obtain a set of image descriptors. By finding the relationship between these descriptors, the Scientific Programming patterns determining the images can be discovered [17,19]. Several classes of features have been considered by researchers and analyzed via ML algorithms to select the most valuable and most effective classification performance.…”

“…Al-jaboriy et al used the nuclear-to-cytoplasmic ratio, nucleus compactness, nucleus form factors, nucleus eccentricity, nucleus elongation, and nucleus rigidity [17,23,24,27]. Among seven studies, which used traditional ML algorithm, four used the SVM method alone and with other algorithms [18][19][20]24] and three utilized ANN and other algorithms [17]. Note that these algorithms are among the most popular algorithms in medical image processing.…”

Classification of Acute Lymphoblastic Leukemia through the Fusion of Local Descriptors

“…Based on peripheral blood smear images, Al-Tahhan [10] intended to develop an improved classification model that is capable to categorize the ALL subtypes. In that article, the cytoplasmic vacuoles and the regularity of the nucleus membrane of ALL cells are the only geometric features that this approach relied on.…”