Classification of White Blood Cells by Deep Learning Methods for Diagnosing Disease

Yıldırım, Muhammed; Çınar, Ahmet

doi:10.18280/ria.330502

Cited by 49 publications

(34 citation statements)

References 15 publications

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“…The purpose of the Median filter is to decrease the hard tone changes in the image and make the image softer [22,23]. The results obtained in the AlexNet model are given in Table 1.…”

Section: Application and Resultsmentioning

confidence: 99%

Classification of Malaria Cell Images with Deep Learning Architectures

Çınar¹,

Yıldırım²

2020

ISI

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Malaria is a contagious disease caused by the infection of erythrocytes by Plasmodium parasites, which are transmitted to human by parasitic female anopheles' mosquitoes during feeding. Malaria is a type of infection that can be fatal if left untreated. It is very important to classify malaria virus images quickly and accurately using computer-aided systems. Because there are not enough personnel in each health unit to perform this procedure, traditional methods are both time consuming and open to errors. Once malaria images have been classified, it will be easier to diagnose malaria virus related diseases. Multiple methods have been developed to process large amounts of data. In particular, deep learning methods are frequently used for classification. In this paper, Convolutional Neural Networks (CNN) have been used to classify malaria images as healthy and parasited. Then, medium filter and gauss filter are applied to the original dataset. When classifying malaria data, the highest accuracy rate is achieved in the DenseNet201 architecture with gaussian filtered data of 97.83%. It is observed that the result obtained with the preprocessed data are higher. The application is implemented in the Matlab environment and works independently of the size of the images in the data set.

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“…The purpose of the Median filter is to decrease the hard tone changes in the image and make the image softer [22,23]. The results obtained in the AlexNet model are given in Table 1.…”

Section: Application and Resultsmentioning

confidence: 99%

Classification of Malaria Cell Images with Deep Learning Architectures

Çınar¹,

Yıldırım²

2020

ISI

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“…Following are the different methods used for pre-processing. Self dual multi-scale morphological toggle (SMTT) block ( Belekar & Chougule, 2015 ), wiener filter ( Patel & Mishra, 2015 ), median filtering ( Elsalamony, 2016 ; Bhanushali et al, 2016 ; Mohite Patil & Bhagavan, 2016 ; ANP and Wildlife Service, 1986 ; Thiruvinal & Ram, 2017 ), Gaussian filtering ( Yildirim & Çinar, 2019 ), gray-scale transformation ( Patel & Mishra, 2015 ; Elsalamony, 2016 ; Bhanushali et al, 2016 ; Mohite Patil & Bhagavan, 2016 ; ANP and Wildlife Service, 1986 ; Bhagavathi & Thomas Niba, 2016 ; Biswas & Ghoshal, 2016 ; Thiruvinal & Ram, 2017 ) which has 3 types viz Linear, Logarithmic and Power–law, histogram stretching ( Elsalamony, 2016 ; Bhanushali et al, 2016 ; Mohite Patil & Bhagavan, 2016 ; ANP and Wildlife Service, 1986 ), green color component from the RGB image ( Negm, Hassan & Kandil, 2018 ), morphological operations ( Elsalamony, 2016 ), edge detection ( Biswas & Ghoshal, 2016 ).…”

Section: Review Overviewmentioning

confidence: 99%

“…This Dataset also includes additional 410 images of WBC and RBC with JPEG and xml metadata format. This database is under MIT license ( Yildirim & Çinar, 2019 ).…”

Section: Review Overviewmentioning

confidence: 99%

A review of microscopic analysis of blood cells for disease detection with AI perspective

Deshpande

Gite

Aluvalu³

2021

PeerJ Computer Science

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Background Any contamination in the human body can prompt changes in blood cell morphology and various parameters of cells. The minuscule images of blood cells are examined for recognizing the contamination inside the body with an expectation of maladies and variations from the norm. Appropriate segmentation of these cells makes the detection of a disease progressively exact and vigorous. Microscopic blood cell analysis is a critical activity in the pathological analysis. It highlights the investigation of appropriate malady after exact location followed by an order of abnormalities, which assumes an essential job in the analysis of various disorders, treatment arranging, and assessment of results of treatment. Methodology A survey of different areas where microscopic imaging of blood cells is used for disease detection is done in this paper. Research papers from this area are obtained from a popular search engine, Google Scholar. The articles are searched considering the basics of blood such as its composition followed by staining of blood, that is most important and mandatory before microscopic analysis. Different methods for classification, segmentation of blood cells are reviewed. Microscopic analysis using image processing, computer vision and machine learning are the main focus of the analysis and the review here. Methodologies employed by different researchers for blood cells analysis in terms of these mentioned algorithms is the key point of review considered in the study. Results Different methodologies used for microscopic analysis of blood cells are analyzed and are compared according to different performance measures. From the extensive review the conclusion is made. Conclusion There are different machine learning and deep learning algorithms employed by researchers for segmentation of blood cell components and disease detection considering microscopic analysis. There is a scope of improvement in terms of different performance evaluation parameters. Different bio-inspired optimization algorithms can be used for improvement. Explainable AI can analyze the features of AI implemented system and will make the system more trusted and commercially suitable.

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“…In this paper, deep learning architectures were used. Deep learning is a type of learning in which algorithms process data and perform the learning process inspired by the human brain [10].…”

Section: Theoritical Backgroundmentioning

confidence: 99%

Classification of Alzheimer's Disease MRI Images with CNN Based Hybrid Method

Yıldırım¹,

Çınar²

2020

ISI

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Alzheimer is a type of dementia disease that is common in older ages. This disease is a progressive form of neurological disease that causes the destruction of brain cells. Since Alzheimer's is a progressive disease, various problems increase over time. For this reason, it is very important to diagnose the disease early and start the treatment process. In this study, it was tried to determine at which stage the disease is or whether it is Alzheimer using brain images. CNN architectures are used to diagnose the disease. In addition, a hybrid method we have developed has been proposed. With the architectures used, it is classified in 4 stages according to the disease progression level. In the proposed hybrid model, the Resnet50 method is used as the basis. The results are obtained separately by Alexnet, Resnet50, Densenet201, Vgg16, and the Hybrid method we developed. An accuracy of 90% has been achieved with the developed hybrid model. Consequently, when other scientific paper in the literature are investigated, it is finalized that the hybrid model developed to diagnose Alzheimer's disease has achieved the success achieved by other CNN architectures and even offers better results.

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Classification of White Blood Cells by Deep Learning Methods for Diagnosing Disease

Cited by 49 publications

References 15 publications

Classification of Malaria Cell Images with Deep Learning Architectures

Classification of Malaria Cell Images with Deep Learning Architectures

A review of microscopic analysis of blood cells for disease detection with AI perspective

Classification of Alzheimer's Disease MRI Images with CNN Based Hybrid Method

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