Leukocyte Classification using Adaptive Neuro-Fuzzy Inference System in Microscopic Blood Images

Rawat, Jiledar; Singh, Annapurna; Bhadauria, H.S.; Virmani, Jitendra; Devgun, Jagtar Singh

doi:10.1007/s13369-017-2959-3

Cited by 24 publications

(9 citation statements)

References 39 publications

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“…Finally, the results of the blood analysis were determined using the values of the essential parameters. The software was able to examine hundreds of cells in one image within a minute [ 23 ]. To measure the performance of the developed OR and MR parameters for leukocyte identification, the F 1 score was calculated.…”

Section: Methodsmentioning

confidence: 99%

“…Recently, much interest has been expressed in the development of systems to automatically classify digital images of peripheral blood smears with high sensitivity and specificity [ 18 , 19 , 20 , 21 ]. Many groups have reported leukocyte classification of microscopic blood images using an adaptive neuro-fuzzy inference system or leukocyte classification based on spatial and spectral features of microscopic hyperspectral images [ 22 , 23 , 24 ]. However, these technologies use high-quality optical microscopes or lenses, which limits their application to low-cost, portable platforms for on-site cell monitoring.…”

Section: Introductionmentioning

confidence: 99%

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Field-Portable Leukocyte Classification Device Based on Lens-Free Shadow Imaging Technique

et al. 2022

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The complete blood count (CBC) is one of the most important clinical steps in clinical diagnosis. The instruments used for CBC are usually expensive and bulky and require well-trained operators. Therefore, it is difficult for medical institutions below the tertiary level to provide these instruments, especially in underprivileged countries. Several reported on-chip blood cell tests are still in their infancy and do not deviate from conventional microscopic or impedance measurement methods. In this study, we (i) combined magnetically activated cell sorting and the differential density method to develop a method to selectively isolate three types of leukocytes from blood and obtain samples with high purity and concentration for portable leukocyte classification using the lens-free shadow imaging technique (LSIT), and (ii) established several shadow parameters to identify the type of leukocytes in a complete leukocyte shadow image by shadow image analysis. The purity of the separated leukocytes was confirmed by flow cytometry. Several shadow parameters such as the “order ratio” and “minimum ratio” were developed to classify the three types of leukocytes. A shadow image library corresponding to each type of leukocyte was created from the tested samples. Compared with clinical reference data, a correlation index of 0.98 was obtained with an average error of 6% and a confidence level of 95%. This technique offers great potential for biological, pharmaceutical, environmental, and clinical applications, especially where point-of-care detection of rare cells is required.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Field-Portable Leukocyte Classification Device Based on Lens-Free Shadow Imaging Technique

et al. 2022

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“…Thus, it is needed to develop more accurate methods to segment blast cells from peripheral blood smears images [79]. Rawat et al [41] have improved the accuracy of blast cell classification to 99.517% using Histogram green color of RGB component for pre-processing of blood smear images and using K-means clustering technique for segmentation of blast cells followed by the extraction of geometry, statistical and textures features and classification by ANN. In the classification phase, different techniques were carried out by researchers to obtain the accurate results, where the ANN and SVM techniques were the most accurate.…”

Section: Discussionmentioning

confidence: 99%

“…Figure 9 shows the difference of image samples from ALL-IDB1, SMC-IDB and IUMS-IDB. Despite the availability of the public datasets, some studies have also used blood smear images from Google or internet [28], [41]- [43]. Table 2 summarizes the publicly available leukemia datasets along with their specifications which are used in the automated detection of leukemia.…”

Section: A Image Acquisitionmentioning

confidence: 99%

Automated Diagnosis of Leukemia: A Comprehensive Review

Shah

Naqvi

Naveed³

et al. 2021

IEEE Access

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Leukemia is the rapid production of abnormal white blood cells that consequently affects the blood and damages the bone marrow. The overproduction of abnormal and immature white blood cells leads to the damage of the immune system due to the reduced production of red blood cells and platelets by the bone marrow of the body. This hematological malignancy is generally diagnosed by manual methods such as complete blood count (CBC), bone marrow aspiration, or microscopic examination of the blood smear. Nevertheless, the manual methods of leukemia diagnosis are economical but are found to be less reliable, time-consuming, and hectic. Technological advancement in the medical field has effectively addressed these issues in the past. The problems in the manual diagnosis of leukemia detection have been overcome by the development of automated methods using the computer-aided diagnostic (CAD) systems for efficient and reliable leukemia diagnosis. Since the last decade, multiple approaches have been proposed for the CAD systems regarding pre-processing, segmentation, feature extraction, feature selection, and for the improvement of the classification accuracy of the CAD system for the leukemia detection. This paper presents a comprehensive review of the CAD systems for the detection of the various types of leukemia. The review presented here entails the details of various CAD systems for the automated diagnosis of various types of leukemia and analyses their methodologies in terms of their efficiency in pre-processing, segmentation, feature extraction and selection, and overall classification accuracy of the CAD system.INDEX TERMS Acute lymphoblastic leukemia (ALL), acute myeloid leukemia (AML), chronic lymphocytic leukemia (CLL), chronic myeloid leukemia (CML), computer-aided diagnostic (CAD), Leukemia.

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“…References [ 7 , 8 , 9 ] addresses many algorithms for the early detection of breast cancer detection. The evaluation of segmentation based on detection rate and accuracy gave the result of breast cancer detection cases [ 10 , 11 , 12 ]. The segmentation leads to feature extraction—the calculation of features based on density, texture, morphology, shape, and size of regions [ 13 , 14 , 15 ].…”

Section: Introductionmentioning

confidence: 99%

An Efficient Segmentation and Classification System in Medical Images Using Intuitionist Possibilistic Fuzzy C-Mean Clustering and Fuzzy SVM Algorithm

Chowdhary

Mittal

Kumaresan

et al. 2020

Sensors

100

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The herpesvirus, polyomavirus, papillomavirus, and retrovirus families are associated with breast cancer. More effort is needed to assess the role of these viruses in the detection and diagnosis of breast cancer cases in women. The aim of this paper is to propose an efficient segmentation and classification system in the Mammography Image Analysis Society (MIAS) images of medical images. Segmentation became challenging for medical images because they are not illuminated in the correct way. The role of segmentation is essential in concern with detecting syndromes in human. This research work is on the segmentation of medical images based on intuitionistic possibilistic fuzzy c-mean (IPFCM) clustering. Intuitionist fuzzy c-mean (IFCM) and possibilistic fuzzy c-mean (PFCM) algorithms are hybridised to deal with problems of fuzzy c-mean. The introduced clustering methodology, in this article, retains the positive points of PFCM which helps to overcome the problem of the coincident clusters, thus the noise and less sensitivity to the outlier. The IPFCM improves the fundamentals of fuzzy c-mean by using intuitionist fuzzy sets. For the clustering of mammogram images for breast cancer detector of abnormal images, IPFCM technique has been applied. The proposed method has been compared with other available fuzzy clustering methods to prove the efficacy of the proposed approach. We compared support vector machine (SVM), decision tree (DT), rough set data analysis (RSDA) and Fuzzy-SVM classification algorithms for achieving an optimal classification result. The outcomes of the studies show that the proposed approach is highly effective with clustering and also with classification of breast cancer. The performance average segmentation accuracy for MIAS images with different noise level 5%, 7% and 9% of IPFCM is 91.25%, 87.50% and 85.30% accordingly. The average classification accuracy rates of the methods (Otsu, Fuzzy c-mean, IFCM, PFCM and IPFCM) for Fuzzy-SVM are 79.69%, 92.19%, 93.13%, 95.00%, and 98.85%, respectively.

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Leukocyte Classification using Adaptive Neuro-Fuzzy Inference System in Microscopic Blood Images

Cited by 24 publications

References 39 publications

Field-Portable Leukocyte Classification Device Based on Lens-Free Shadow Imaging Technique

Field-Portable Leukocyte Classification Device Based on Lens-Free Shadow Imaging Technique

Automated Diagnosis of Leukemia: A Comprehensive Review

An Efficient Segmentation and Classification System in Medical Images Using Intuitionist Possibilistic Fuzzy C-Mean Clustering and Fuzzy SVM Algorithm

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