Computer-aided system for Leukocyte nucleus segmentation and Leukocyte classification based on nucleus characteristics

Sapna, S.; Renuka, A.

doi:10.1080/1206212x.2020.1726013

Cited by 10 publications

(7 citation statements)

References 19 publications

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“…They segmented the nucleus using Fuzzy C-means clustering and extracted geometrical/colour/texture features. They reported 92.8% and 91.5% accuracy for FFNN and SVM, respectively (Sapna & Renuka, 2020). Togacar et al classified WBCs using deep features.…”

Section: Literature Reviewmentioning

confidence: 99%

Intelligent algorithm for detection of dengue using mobilenetv2‐based deep features with lymphocyte nucleus

et al. 2021

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Dengue is a vector-borne disease that is highly endemic in countries located in tropical regions. It can cause severe complications and can even lead to death in the case of delayed diagnosis. Detection of dengue is done by manually examining the platelets and lymphocytes in Leishman's stained peripheral blood smear (PBS) images. PBS examination is considered the gold standard for diagnosing various haematological disorders. However, manual analysis of the PBS is labour-intensive, tedious, and time-consuming, requiring a skilled and experienced haematologist. Today, soft computing methods and artificial intelligence have made their way into every science and technology branch. One such area which has adopted this approach is digital pathology, for automatically identifying and diagnosing various diseases. The main objective of this work was to design an intelligent algorithm to classify normal and dengue patients with the help of digital microscopic blood smear images. A total of 94 normal and dengue-infected PBSs were acquired at a magnification of 100Â. Grey-level segmentation based on Otsu's thresholding was used for the segmentation of the nucleus of lymphocytes. Distinct features from the nucleus that differentiated infected cells from normal were extracted using a pre-trained MobileNetV2 network and local binary pattern. Significant features were selected using the ReliefF algorithm. Subsequently, these features were fed to the support vector machine (SVM) classifier. Our proposed system gave an accuracy, sensitivity, and specificity of 95.74%, 98.14%, and 92.50%, respectively. Hence, the developed intelligent model with deep and hand-crafted features can be valuable for dengue diagnosis.

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Section: Literature Reviewmentioning

confidence: 99%

Intelligent algorithm for detection of dengue using mobilenetv2‐based deep features with lymphocyte nucleus

et al. 2021

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“…In Table 11, a comparison is assessed between results achieved by existing techniques and the proposed model. The existing work has been employed for the detection of WBCs with 88.1% accuracy [79]. A semantic segmentation for recognition and localization of leukocytes using Deeplab V3+ with ResNet 50 is utilized [77].…”

Section: Experiment#2 Segmentation Of Wbcsmentioning

confidence: 99%

A deep network designed for segmentation and classification of leukemia using fusion of the transfer learning models

Saleem

Amin

Sharif

et al. 2021

Complex Intell. Syst.

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White blood cells (WBCs) are a portion of the immune system which fights against germs. Leukemia is the most common blood cancer which may lead to death. It occurs due to the production of a large number of immature WBCs in the bone marrow that destroy healthy cells. To overcome the severity of this disease, it is necessary to diagnose the shapes of immature cells at an early stage that ultimately reduces the modality rate of the patients. Recently different types of segmentation and classification methods are presented based upon deep-learning (DL) models but still have some limitations. This research aims to propose a modified DL approach for the accurate segmentation of leukocytes and their classification. The proposed technique includes two core steps: preprocessing-based classification and segmentation. In preprocessing, synthetic images are generated using a generative adversarial network (GAN) and normalized by color transformation. The optimal deep features are extracted from each blood smear image using pretrained deep models i.e., DarkNet-53 and ShuffleNet. More informative features are selected by principal component analysis (PCA) and fused serially for classification. The morphological operations based on color thresholding with the deep semantic method are utilized for leukemia segmentation of classified cells. The classification accuracy achieved with ALL-IDB and LISC dataset is 100% and 99.70% for the classification of leukocytes i.e., blast, no blast, basophils, neutrophils, eosinophils, lymphocytes, and monocytes, respectively. Whereas semantic segmentation achieved 99.10% and 98.60% for average and global accuracy, respectively. The proposed method achieved outstanding outcomes as compared to the latest existing research works.

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“…where A seg is the segmented area using the automated segmentation method, while A GT is the segmented area by the hematological expert. Yet, in studies [32], [46], [47], the segmentation process mainly targeted the WBC nuclei in order to discriminate between the WBC classes. But, in our study we have considered the segmentation of the whole leukocyte cell (i.e.…”

Section: Performance Evaluation Comparison With State-of-the-art Workmentioning

confidence: 99%

“…Inspired by the variety of WBC characteristics [32], and since the segmentation and classification processes are highly related, we aim to establish a new framework by exploiting the benefits of these variations and incorporating them in a tailored segmentation algorithm for each class. The proposed system adopts an inverse flow compared with traditional computer-aided detection systems.…”

Section: Introductionmentioning

confidence: 99%

A Cascaded Classification-Segmentation Reversible System for Computer-Aided Detection and Cells Counting in Microscopic Peripheral Blood Smear Basophils and Eosinophils Images

2021

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Computer-aided image analysis has a pivotal role in automated counting and classification of white blood cells (WBCs) in peripheral blood images. Due to their different characteristics, our proposed approach is based on investigating the variations between the basophils and eosinophils in terms of their color histogram, size, and shape before performing the segmentation process. Accordingly, we proposed a cascaded system using a classification-based segmentation process, called classification-segmentation reversible system (CSRS). Prior to applying the CSRS system, a Histogram-based Object to Background Disparity (HOBD) metric was deduced to determine the most appropriate color plane for performing the initial WBC detection (first segmentation). Investigating the local histogram features of both classes resulted in a 92.4% initial classification accuracy using the third-degree polynomial support vector machine (SVM) method. Subsequently, in the proposed CSRS approach, transformation-based segmentation algorithms were developed to fit the specific requirements of each of the two predicted classes. The proposed CSRS system is used, where the images from an initial classification process are fed into a second segmentation process for each class separately. The segmentation results demonstrated a similarity index of 94.9% for basophils, and 94.1% for eosinophils. Moreover, an average counting accuracy of 97.4% for both classes was achieved. In addition, a second classification was carried out after applying the CSRS, achieving a 5.2% increase in accuracy compared to the initial classification process.

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Computer-aided system for Leukocyte nucleus segmentation and Leukocyte classification based on nucleus characteristics

Cited by 10 publications

References 19 publications

Intelligent algorithm for detection of dengue using mobilenetv2‐based deep features with lymphocyte nucleus

Intelligent algorithm for detection of dengue using mobilenetv2‐based deep features with lymphocyte nucleus

A deep network designed for segmentation and classification of leukemia using fusion of the transfer learning models

A Cascaded Classification-Segmentation Reversible System for Computer-Aided Detection and Cells Counting in Microscopic Peripheral Blood Smear Basophils and Eosinophils Images

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