Abstract
Acute myeloid leukemia (AML) is a fatal blood cancer that progresses rapidly and hinders the function of blood cells and the immune system. The current AML diagnostic method, a manual examination of the peripheral blood smear, is time consuming, labor intensive, and suffers from considerable inter-observer variation. Herein, a machine learning model to detect and classify immature leukocytes for efficient diagnosis of AML is presented. Images of leukocytes in AML patients and healthy controls were obtained from a publicly available dataset in The Cancer Imaging Archive. Image format conversion, multi-Otsu thresholding, and morphological operations were used for segmentation of the nucleus and cytoplasm. From each image, 16 features were extracted, two of which are new nucleus color features proposed in this study. A random forest algorithm was trained for the detection and classification of immature leukocytes. The model achieved 92.99% accuracy for detection and 93.45% accuracy for classification of immature leukocytes into four types. Precision values for each class were above 65%, which is an improvement on the current state of art. Based on Gini importance, the nucleus to cytoplasm area ratio was a discriminative feature for both detection and classification, while the two proposed features were shown to be significant for classification. The proposed model can be used as a support tool for the diagnosis of AML, and the features calculated to be most important serve as a baseline for future research.
Highlights
Acute myeloid leukemia (AML) is the deadliest of the four types of leukemia, accounting for 11,000 annual deaths in the US with an average five-year survival rate of 28.7% [1]
The model was capable of detecting immature leukocytes with 93% accuracy and 0.98 area under curve (AUC)-receiver operating characteristic (ROC), which is on par with the current state of art [12]
The model achieved precision of above 65% for each of the four immature leukocyte classes during multiclass classification, despite imbalance in numbers across classes, which is an improvement over previous research
Summary
Acute myeloid leukemia (AML) is the deadliest of the four types of leukemia, accounting for 11,000 annual deaths in the US with an average five-year survival rate of 28.7% [1]. AML is characterized by the overproduction and accumulation of immature leukocytes, myeloid precursors, in the bone marrow and peripheral blood. The immature white blood cells prevent the functions of the bone marrow, including the production of red blood cells and platelets, which makes the immune system vulnerable [2,3]. Detecting and classifying immature leukocytes is crucial for the diagnosis of AML. Microscopic examination of peripheral blood smears is the standard procedure for the diagnosis of leukemia, but other procedures are used [5]. Manual blood smear examination is labor intensive and time consuming [6]. Manual examination is prone to considerable inter- and intra-observer variation of standards, as well as biases such as tiredness and operator experience [7]. Depending on the experience of the hematologist, manual examination has an error rate of 30% to 40% [8]
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