%0 Journal Article
%@ 2227-9032
%A Rustam, Furqan
%A Aslam, Naila
%A De La Torre Díez, Isabel
%A Khan, Yaser Daanial
%A Vidal Mazón, Juan Luis
%A Rodríguez Velasco, Carmen Lilí
%A Ashraf, Imran
%D 2022
%F unic:4607
%J Healthcare
%K white blood cells classification; leukemia; texture features; Chi-squared; SMOTE
%N 11
%P 2230
%T White Blood Cell Classification Using Texture and RGB Features of Oversampled Microscopic Images
%U http://repositorio.unic.co.ao/id/eprint/4607/
%V 10
%X White blood cell (WBC) type classification is a task of significant importance for diagnosis using microscopic images of WBC, which develop immunity to fight against infections and foreign substances. WBCs consist of different types, and abnormalities in a type of WBC may potentially represent a disease such as leukemia. Existing studies are limited by low accuracy and overrated performance, often caused by model overfit due to an imbalanced dataset. Additionally, many studies consider a lower number of WBC types, and the accuracy is exaggerated. This study presents a hybrid feature set of selective features and synthetic minority oversampling technique-based resampling to mitigate the influence of the above-mentioned problems. Furthermore, machine learning models are adopted for being less computationally complex, requiring less data for training, and providing robust results. Experiments are performed using both machine- and deep learning models for performance comparison using the original dataset, augmented dataset, and oversampled dataset to analyze the performances of the models. The results suggest that a hybrid feature set of both texture and RGB features from microscopic images, selected using Chi2, produces a high accuracy of 0.97 with random forest. Performance appraisal using k-fold cross-validation and comparison with existing state-of-the-art studies shows that the proposed approach outperforms existing studies regarding the obtained accuracy and computational complexity.