Deep learning-based image annotation for leukocyte segmentation and classification of blood cell morphology.
BMC Med Imaging
; 24(1): 83, 2024 Apr 08.
Article
em En
| MEDLINE
| ID: mdl-38589793
ABSTRACT
The research focuses on the segmentation and classification of leukocytes, a crucial task in medical image analysis for diagnosing various diseases. The leukocyte dataset comprises four classes of images such as monocytes, lymphocytes, eosinophils, and neutrophils. Leukocyte segmentation is achieved through image processing techniques, including background subtraction, noise removal, and contouring. To get isolated leukocytes, background mask creation, Erythrocytes mask creation, and Leukocytes mask creation are performed on the blood cell images. Isolated leukocytes are then subjected to data augmentation including brightness and contrast adjustment, flipping, and random shearing, to improve the generalizability of the CNN model. A deep Convolutional Neural Network (CNN) model is employed on augmented dataset for effective feature extraction and classification. The deep CNN model consists of four convolutional blocks having eleven convolutional layers, eight batch normalization layers, eight Rectified Linear Unit (ReLU) layers, and four dropout layers to capture increasingly complex patterns. For this research, a publicly available dataset from Kaggle consisting of a total of 12,444 images of four types of leukocytes was used to conduct the experiments. Results showcase the robustness of the proposed framework, achieving impressive performance metrics with an accuracy of 97.98% and precision of 97.97%. These outcomes affirm the efficacy of the devised segmentation and classification approach in accurately identifying and categorizing leukocytes. The combination of advanced CNN architecture and meticulous pre-processing steps establishes a foundation for future developments in the field of medical image analysis.
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Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Aprendizado Profundo
Limite:
Humans
Idioma:
En
Revista:
BMC Med Imaging
Assunto da revista:
DIAGNOSTICO POR IMAGEM
Ano de publicação:
2024
Tipo de documento:
Article
País de afiliação:
Índia