Automatic classification of white blood cells using deep features based convolutional neural network

Meenakshi, A.; Ruth, J. Anitha; Kanagavalli, V. R.; Uma, R.

doi:10.1007/s11042-022-12539-2

Cited by 13 publications

(4 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…By utilizing convolutional neural networks, efficient classification can be prepared using automatic feature extraction [37] [38]- [41]. In deep learning methods [42]- [45], pre-trained models such as AlexNet, GoogleNet [46], ResNet [47], VGG-16 [48], Inception V3 [49], and many others extract and select features. Based on the simulation of electron spectra for surface analysis (SESSA) algorithm, Ahmed et al Proposed a method for extracting WBCs features using a robust CNN architecture called VGGNet.…”

Section: Pretraind Vgg16mentioning

confidence: 99%

Lightweight EfficientNetB3 Model Based on Depthwise Separable Convolutions for Enhancing Classification of Leukemia White Blood Cell Images

Batool

Byun

2023

IEEE Access

View full text Add to dashboard Cite

Acute lymphoblastic leukemia (ALL) occurs when undeveloped lymphocytes are grown excessively in the bone marrow due to the formation of many immature white blood cells (WBC) in the bone marrow to destroy the healthy cell, which may lead to death. White blood cells are part of the immune system fighting against germs. A timely and accurate cancer diagnosis is important for effective treatment to improve survival rates. Since the image of acute lymphoblastic leukemia cells (cancer cells) under the microscope is complicated to recognize the difference between ALL cancer cells and normal cells. In order to reduce the severity of this disease, it is necessary to classify immature cells at an early stage. In recent years, different classification models have been introduced based on machine learning (ML) and deep learning (DL) algorithms, but they need to be improved. This work enhances the diagnosis of ALL with a computer-aided system that yields accurate results by using deep learning techniques. This research proposed a lightweight DL-assisted robust model based on EfficientNet-B3 using depthwise separable convolutions for classifying acute lymphoblastic leukemia and normal cells in the white blood cell images dataset. The main objective of the proposed lightweight EfficientNet-B3 is to utilize less trainable parameters to enhance the performance and efficiency of the classification of leukemia. Furthermore, two publicly available datasets are considered to evaluate the effectiveness and generalization of the proposed lightweight EfficientNet-B3. In addition, different measures are employed to evaluate the effectiveness and efficiency of the proposed classifier for the accurate and reliable classification of leukaemia cells. In addition, a detailed analysis is given to evaluate and compare the performance and efficiency of the proposed with existing pre-trained and ensemble DL classifiers. Based on experimental results, it is investigated that the proposed model for image classification achieves better performance and outperforms the existing benchmark DL and other ensemble classifiers. Moreover, our finding suggests that the proposed lightweight EfficientNet-B3 model is reliable and generalized to facilitate clinical research and practitioners for leukemia detection. INDEX TERMS Acute lymphoblastic leukemia (ALL), EfficientNet-B3, CNN, White blood cell image classification, Deep Learning I. INTRODUCTIONLeukemia is a type of blood cancer that affect the white blood cells to become cancerous. The immune system of the body is facing the risk of these abnormal blood cells, which affect the bone marrow and white and red blood cells. Bone marrow cancer affects children and teenagers. Acute leukemia has two types: acute lymphoblastic leukemia (ALL) and acute myeloid leukemia (AML). Lymphocytes,

show abstract

Section: Pretraind Vgg16mentioning

confidence: 99%

Lightweight EfficientNetB3 Model Based on Depthwise Separable Convolutions for Enhancing Classification of Leukemia White Blood Cell Images

Batool

Byun

2023

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Meenakshi et al [30] have developed a method for the microscopic evaluation of ALL diagnoses. The proposed scheme employs a stimulating segmentation and detection scheme based on the measurement of WBCs.…”

Section: Related Workmentioning

confidence: 99%

Histogram-Based Decision Support System for Extraction and Classification of Leukemia in Blood Smear Images

Veeraiah¹,

Alotaibi²,

Subahi³

2023

Computer Systems Science and Engineering

View full text Add to dashboard Cite

An abnormality that develops in white blood cells is called leukemia. The diagnosis of leukemia is made possible by microscopic investigation of the smear in the periphery. Prior training is necessary to complete the morphological examination of the blood smear for leukemia diagnosis. This paper proposes a Histogram Threshold Segmentation Classifier (HTsC) for a decision support system. The proposed HTsC is evaluated based on the color and brightness variation in the dataset of blood smear images. Arithmetic operations are used to crop the nucleus based on automated approximation. White Blood Cell (WBC) segmentation is calculated using the active contour model to determine the contrast between image regions using the color transfer approach. Through entropy-adaptive mask generation, WBCs accurately detect the circularity region for identification of the nucleus. The proposed HTsC addressed the cytoplasm region based on variations in size and shape concerning addition and rotation operations. Variation in WBC imaging characteristics depends on the cytoplasmic and nuclear regions. The computation of the variation between image features in the cytoplasm and nuclei regions of the WBCs is used to classify blood smear images. The classification of the blood smear is performed with conventional machine-learning techniques integrated with the features of the deep-learning regression classifier. The designed HTsC classifier comprises the binary classifier with the classification of the lymphocytes, monocytes, neutrophils, eosinophils, and abnormalities in the WBCs. The proposed HTsC identifies the abnormal activity in the WBC, considering the color and shape features. It exhibits a higher classification accuracy value of 99.6% when combined with the other classifiers. The comparative analysis expressed that the proposed HTsC model exhibits an overall accuracy value of 98%, which is approximately 3%-12% higher than the conventional technique.

show abstract

“…It constitutes around 7% of total body weight in younger people. It is made up of 55% plasma, allowing it to circulate easily all over the body via the arteries [1][2][3]. The cells in the blood are divided into three types, which differ in their color, size, texture, morphology, and composition: thrombocytes (platelets), leukocytes (white blood cells, WBCs), and erythrocytes (red blood cells, RBCs).…”

Section: Introductionmentioning

confidence: 99%

“…(1) An effective method is proposed for the Nano Biomed. Eng., 2023, 15 (2) segmentation and feature extraction of WBC images using SegNet-and EfficientNet-based networks.…”

Section: Introductionmentioning

confidence: 99%

EfficientNet - XGBoost: An Effective White-Blood-Cell Segmentation and Classification Framework

SivaRao,

Rao

2023

Nano Biomedicine and Engineering

View full text Add to dashboard Cite

In the human body, white blood cells (WBCs) are crucial immune cells that help in the early detection of a variety of illnesses. Determination of the number of WBCs can be used to diagnose conditions such as hematological, immunological, and autoimmune diseases, as well as AIDS and leukemia. However, the conventional method of classifying and counting WBCs is time-consuming, laborious, and potentially erroneous. Therefore, this paper presents a computer-assisted automated method for recognizing and detecting WBC categories from blood images. Initially, the blood cell image is preprocessed and then segmented using an effective deep learning architecture called SegNet. Then, the important features are devised and extracted using the EfficientNet architecture. Finally, the WBCs are categorized into four different types using the XGBoost classifier: neutrophils, eosinophils, monocytes, and lymphocytes. The advantages of SegNet, EfficientNet, and XGBoost make the proposed model more robust and achieve a more efficient classification of the WBCs. The BCCD dataset is used to evaluate the performance of the proposed methodology, and the findings are compared to existing state-of-the-art approaches based on accuracy, precision, sensitivity, specificity, and F1-score. Evaluation results show that the proposed approach has a higher rank-1 accuracy of 99.02% and outperformed other existing techniques.

show abstract

Automatic classification of white blood cells using deep features based convolutional neural network

Cited by 13 publications

References 38 publications

Lightweight EfficientNetB3 Model Based on Depthwise Separable Convolutions for Enhancing Classification of Leukemia White Blood Cell Images

Lightweight EfficientNetB3 Model Based on Depthwise Separable Convolutions for Enhancing Classification of Leukemia White Blood Cell Images

Histogram-Based Decision Support System for Extraction and Classification of Leukemia in Blood Smear Images

EfficientNet - XGBoost: An Effective White-Blood-Cell Segmentation and Classification Framework

Contact Info

Product

Resources

About