Leukemia Prediction from Microscopic Images of Human Blood Cell Using HOG Feature Descriptor and Logistic Regression

Leukemia originates in bone marrow. It massively affects the production of appropriate blood cells. Hence, its early detection is very crucial for human living. Generally, computational approaches for Leukemia detection use microscopic blood cells images. Then, machine learning based models are trained and tested for accurate measurement. The main challenge here is to achieve an acceptable accuracy with a scalable method. However, data inconsistency, missing values and data incompleteness made the researchers' job much more difficult. In these consequences, this paper proposes a scalable Leukemia prediction method based on a publicly available ALL_IDB dataset using the HOG feature descriptor and Logistic Regression. Initially, the proposed method used Canny edge detector and noise reduction operators to detect the exact shape of Lymphocytes. Then, Principal Component Analysis (PCA) is applied to the detected image shapes. The PCA reduces the data dimensions without losing any valuable information and thus greatly minimizes the afterward computational cost. Finally, a classifier based model is produced for unforeseen events and it is tested. The results are validated using n-fold cross-validation technique, where n is a positive integer greater than or equal to three. The maximum average accuracy of the proposed model is 96% which is much higher than the state-of-the-art schemes.