White blood cells classification using multi-fold pre-processing and optimized CNN model.
Sci Rep
; 14(1): 3570, 2024 02 12.
Article
en En
| MEDLINE
| ID: mdl-38347011
ABSTRACT
White blood cells (WBCs) play a vital role in immune responses against infections and foreign agents. Different WBC types exist, and anomalies within them can indicate diseases like leukemia. Previous research suffers from limited accuracy and inflated performance due to the usage of less important features. Moreover, these studies often focus on fewer WBC types, exaggerating accuracy. This study addresses the crucial task of classifying WBC types using microscopic images. This study introduces a novel approach using extensive pre-processing with data augmentation techniques to produce a more significant feature set to achieve more promising results. The study conducts experiments employing both conventional deep learning and transfer learning models, comparing performance with state-of-the-art machine and deep learning models. Results reveal that a pre-processed feature set and convolutional neural network classifier achieves a significantly better accuracy of 0.99. The proposed method demonstrates superior accuracy and computational efficiency compared to existing state-of-the-art works.
Texto completo:
1
Banco de datos:
MEDLINE
Asunto principal:
Leucemia
/
Leucocitos
Límite:
Humans
Idioma:
En
Año:
2024
Tipo del documento:
Article