Classification of White blood cell using Convolution Neural Network

Girdhar, Ashish; Kapur, Himani; Kumar, Vijay

doi:10.1016/j.bspc.2021.103156

Cited by 52 publications

(26 citation statements)

References 19 publications

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“…Females showed much higher ESR level as compare to males. Current studies, showed that hemoglobin level, erythrocytes, platelets and leucocytes were important in cancer diagnosis because, all these factor directly or indirectly linked with immunity of person [31].…”

Section: Resultsmentioning

confidence: 99%

Blueprinting in Coherence to Diagnostic Momentum: On Account of Cancer Detection in Bahawalpur Community

Manzoor¹,

Dilshad²,

Hassan³

et al. 2022

PJBB

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In the current era, cancer is a critical health problem. Purpose of the study was symptomatic analysis of cancer patients and medical evaluation of hematological and biochemical parameters of patients from different geographical areas of district Bahawalpur. A crosssectional study was done to find out the diagnostic timeliness in cancer patients. Out of 150 cancer patients, 53% patients were males and 47% females with age from 15 years to 75 years. It has been shown that 76% of patients represent major symptoms of cancer while 24% of patients were showing minor symptoms. Furthermore, cancer patients were disturbed metabolical, hematological, and biochemical profiling. There were 83% of patients with anemia and 17% patients have normal hemoglobin count in males and 85 % of total female patients have low hemoglobin level while rest of 15% have normal hemoglobin count. Illiteracy is positively influencing the factors responsible for causing cancer with 53%, whereas 23% less than primary level education and only 1% of patients had completed their graduation. There were 31% of the patients lies in upper lower class of society, 28% lower class, Lower middle class 19%, middle class 14%, upper middle class 7% and only 1 % patients belong to high social status. This study will help to diagnosis the early cancer detection via biochemical and metabolic profiling.

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Section: Resultsmentioning

confidence: 99%

Blueprinting in Coherence to Diagnostic Momentum: On Account of Cancer Detection in Bahawalpur Community

Manzoor¹,

Dilshad²,

Hassan³

et al. 2022

PJBB

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“…Bu model %98.84 doğruluk, %99.33 kesinlik, %98.85 hassasiyet ve %99.61 özgüllük vermiştir [18]. Ayrıca Girdhar vd., tarafından yapılan bir çalışmada da BKH tipini diğer yaklaşımlardan çok daha az iterasyonla sınıflandırabildiği belirtilen bir ESA yaklaşımı önerilmiştir [19]. Yu vd., yılında yaptıkları çalışmada BKH sınıflandırması için ResNet50, Inception V3, VGG16, VGG19, Xception yazılımlarını kullanmış ve diğer sınıflandırma yöntemleri arasından % 88.5'lik doğruluk oranıyla en yüksek değerin ESA yöntemiyle elde edildiğini belirtmiştir [20].…”

Section: İlgili çAlışmalarunclassified

Lökosi̇t Tespi̇ti̇ İçi̇n Beyaz Kan Hücreleri̇ni̇n Esa Kullanilarak Siniflandirilmasi

Karakuş

Özbay

2022

Adıyaman Üniversitesi Mühendislik Bilimleri Dergisi

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Beyaz kan hücreleri, insanların bağışıklık sisteminin en önemli yapısı olup, kan ve lenf dokularında kemik iliği tarafından üretilmektedir. Bu hücreler insan vücudunu hastalık ve yabancı organizmalara karşı koruyan savunma mekanizmalarıdır ve kandaki oranı düştüğünde Lökopeni ile karşılaşılabilir. Bu hücrelerin insan vücudundaki oranının belirlenmesi ve hastalığın tespit ve tedavisi için yoğun emek harcaması gerekmektedir. Bu çalışmada, derin öğrenme modellerini kullanarak beyaz kan hücreleri sınıflandırma performansının iyileştirilmesi amaçlanmıştır. Sınıflandırma işlemini daha verimli gerçekleştirmek için evrişimli sinir ağı modelleri kullanılmıştır. Beyaz kan hücresi çeşitleri olan eozinofil, lenfosit, monosit ve nötrofil arasında ayrım yapmak için Densenet201, ResNet50 ve Alexnet birleştirilmiştir. Elde edilen özellik haritalarının sınıflandırılması için K-En yakın komşuluk, Destek Vektör Makinesi ve Naïve Bayes olmak üzere üç farklı makine öğrenmesi sınıflandırıcısı kullanılmıştır. Derin Öğrenme (DÖ) ile eğitilen Kaggle veri kümesi görüntülerine CLAHE ve Gauss filtreleri uygulanarak bu görüntüler üç ESA ağı ile yeniden sınıflandırılmıştır. Bu filtreler uygulandıktan sonra elde edilen sonuçların, orijinal verilerle elde edilen sınıflandırma sonuçlardan daha yüksek olduğu ortaya konmuştur.

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“… 15 Girdhar et al (2022) also employed the CNN model to classify white blood cell. 16 Davamani et al (2022) developed fuzzy c-means clustering for blood cell classification. 17 In addition, others have developed AI-based models to segment blood vessels.…”

Section: Introductionmentioning

confidence: 99%

“…Based on what was said above, machine learning (ML), which has been proven to be an efficient method to replace common statistical approaches and a new modeling tool in solving complicated regression and classification problems, could be used to estimate the RI of hemoglobin as well. In this realm, in the classification task, a number of studies were conducted to successfully classify the blood cells. − Tomari et al(2014) used an artificial neural network (ANN) to classify red blood cells as normal/abnormal . Kultu et al (2020) proposed a convolutional neural network (CNN) to identify and locate white blood cell types in blood images, which led to an increase in the performance of existing blood test devices .…”

Section: Introductionmentioning

confidence: 99%

“…To address the issue of multiple cell overlap in white blood cell image classification, Patil et al (2021) combined the deep learning approach (merging of the CNN and recurrent neural network model) with canonical correlation analysis . Girdhar et al (2022) also employed the CNN model to classify white blood cell . Davamani et al (2022) developed fuzzy c-means clustering for blood cell classification .…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Refractive Index of Hemoglobin Analysis: A Comparison of Alternating Conditional Expectations and Computational Intelligence Models

Alizamir

Gholami

Bahrami³

et al. 2022

ACS Omega

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Hemoglobin is one of the most important blood elements, and its optical properties will determine all other optical properties of human blood. Since the refractive index (RI) of hemoglobin plays a vital role as a non-invasive indicator of some illnesses, accurate calculation of it would be of great importance. Moreover, measurement of the RI of hemoglobin in the laboratory is time-consuming and expensive; thus, developing a smart approach to estimate this parameter is necessary. In this research, four viable strategies were used to make a quantitative correlation between the RI of hemoglobin and its influencing parameters including the concentration, wavelength, and temperature. First, alternating conditional expectations (ACE), a statistical approach, was employed to generate a correlation to predict the RI of hemoglobin. Then, three different optimized intelligent techniques—optimized neural network (ONN), optimized fuzzy inference system (OFIS), and optimized support vector regression (OSVR)—were used to model the RI. A bat-inspired (BA) algorithm was embedded in the formulation of intelligent models to obtain the optimal values of weights and biases of an artificial neural network, membership functions of the fuzzy inference system, and free parameters of support vector regression. The coefficient of determination, root-mean-square error, average absolute relative error, and symmetric mean absolute percentage error for each of the ACE, ONN, OFIS, and OSVR were found as the measure of each model’s accuracy. Results showed that ACE and optimized models (ONN, OFIS, and OSVR) have promising results in the estimation of hemoglobin’s RI. Collectively, ACE outperformed ONN, OFIS, and OSVR, while sensitivity analysis indicated that the concentration, wavelength, and, lastly, temperature would have the highest impact on the RI.

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Classification of White blood cell using Convolution Neural Network

Cited by 52 publications

References 19 publications

Blueprinting in Coherence to Diagnostic Momentum: On Account of Cancer Detection in Bahawalpur Community

Blueprinting in Coherence to Diagnostic Momentum: On Account of Cancer Detection in Bahawalpur Community

Lökosi̇t Tespi̇ti̇ İçi̇n Beyaz Kan Hücreleri̇ni̇n Esa Kullanilarak Siniflandirilmasi

Refractive Index of Hemoglobin Analysis: A Comparison of Alternating Conditional Expectations and Computational Intelligence Models

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