TY  - JOUR
A1  - Rustam, Furqan
A1  - Aslam, Naila
A1  - De La Torre Díez, Isabel
A1  - Khan, Yaser Daanial
A1  - Vidal Mazón, Juan Luis
A1  - Rodríguez Velasco, Carmen Lilí
A1  - Ashraf, Imran
IS  - 11
AV  - public
TI  - White Blood Cell Classification Using Texture and RGB Features of Oversampled Microscopic Images
Y1  - 2022/11//
N2  - 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.
KW  - white blood cells classification; leukemia; texture features; Chi-squared; SMOTE
ID  - uninipr4607
JF  - Healthcare
SN  - 2227-9032
UR  - http://doi.org/10.3390/healthcare10112230
VL  - 10
ER  -