relation: http://repositorio.unic.co.ao/id/eprint/4607/ canonical: http://repositorio.unic.co.ao/id/eprint/4607/ title: White Blood Cell Classification Using Texture and RGB Features of Oversampled Microscopic Images creator: Rustam, Furqan creator: Aslam, Naila creator: De La Torre Díez, Isabel creator: Khan, Yaser Daanial creator: Vidal Mazón, Juan Luis creator: Rodríguez Velasco, Carmen Lilí creator: Ashraf, Imran subject: Biomedicina subject: Ingeniería description: 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. date: 2022-11 type: Artículo type: PeerReviewed format: text language: en rights: cc_by_4 identifier: http://repositorio.unic.co.ao/id/eprint/4607/1/healthcare-10-02230%20%281%29.pdf identifier: Artículo Materias > Biomedicina Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica Universidad Internacional Iberoamericana México > Investigación > Revistas Científicas Universidad Internacional Iberoamericana Puerto Rico > Investigación > Producción Científica Universidad Internacional do Cuanza > Investigación > Producción Científica Abierto Inglés 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. metadata Rustam, Furqan; Aslam, Naila; De La Torre Díez, Isabel; Khan, Yaser Daanial; Vidal Mazón, Juan Luis; Rodríguez Velasco, Carmen Lilí y Ashraf, Imran mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, juanluis.vidal@uneatlantico.es, carmen.rodriguez@uneatlantico.es, SIN ESPECIFICAR (2022) White Blood Cell Classification Using Texture and RGB Features of Oversampled Microscopic Images. Healthcare, 10 (11). p. 2230. ISSN 2227-9032 relation: http://doi.org/10.3390/healthcare10112230 relation: doi:10.3390/healthcare10112230 language: en