An Efficient Implementation of Acute Lymphoblastic Leukemia Images segmentation on the FPGA

ElDahshan, Kamal A.; Youssef, Mohamed S.; Masameer, Emad; Mustafa, Mohammed Ahmed

doi:10.14738/aivp.33.1196

Cited by 5 publications

(3 citation statements)

References 23 publications

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“…This errand is inalienably challenging due to the different run of cell shapes, sizes, and the potential cover of cells in thick regions of the images. Over a long time, analysts have investigated numerous approaches to address these challenges [ 4 – 6 ].…”

Section: Introductionmentioning

confidence: 99%

Deep learning-based image annotation for leukocyte segmentation and classification of blood cell morphology

Anand,

Gupta,

Koundal

et al. 2024

BMC Med Imaging

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The research focuses on the segmentation and classification of leukocytes, a crucial task in medical image analysis for diagnosing various diseases. The leukocyte dataset comprises four classes of images such as monocytes, lymphocytes, eosinophils, and neutrophils. Leukocyte segmentation is achieved through image processing techniques, including background subtraction, noise removal, and contouring. To get isolated leukocytes, background mask creation, Erythrocytes mask creation, and Leukocytes mask creation are performed on the blood cell images. Isolated leukocytes are then subjected to data augmentation including brightness and contrast adjustment, flipping, and random shearing, to improve the generalizability of the CNN model. A deep Convolutional Neural Network (CNN) model is employed on augmented dataset for effective feature extraction and classification. The deep CNN model consists of four convolutional blocks having eleven convolutional layers, eight batch normalization layers, eight Rectified Linear Unit (ReLU) layers, and four dropout layers to capture increasingly complex patterns. For this research, a publicly available dataset from Kaggle consisting of a total of 12,444 images of four types of leukocytes was used to conduct the experiments. Results showcase the robustness of the proposed framework, achieving impressive performance metrics with an accuracy of 97.98% and precision of 97.97%. These outcomes affirm the efficacy of the devised segmentation and classification approach in accurately identifying and categorizing leukocytes. The combination of advanced CNN architecture and meticulous pre-processing steps establishes a foundation for future developments in the field of medical image analysis.

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Section: Introductionmentioning

confidence: 99%

Deep learning-based image annotation for leukocyte segmentation and classification of blood cell morphology

Anand,

Gupta,

Koundal

et al. 2024

BMC Med Imaging

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show abstract

“…Leukemia is a type of cancer that affects blood, lymphocyte system, and bone marrow. Thus, the correct segmentation of WBCs has an impact on further steps, such as feature extraction and classification, to obtain results more accurately [3]- [5]. In general, the main components of blood are four types as follows:…”

Section: Introductionmentioning

confidence: 99%

Thresholding-based White Blood Cells Segmentation from Microscopic Blood Images

Mohammed

Abdulla²

2020

UHD J SCI TECH

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Digital image processing has a significant role in different research areas, including medical image processing, object detection, biometrics, information hiding, and image compression. Image segmentation, which is one of the most important steps in processing medical image, makes the objects inside images more meaningful. For example, from microscopic images, blood cancer can be identified which is known as leukemia; for this purpose at first, the white blood cells (WBCs) need to be segmented. This paper focuses on developing a segmentation technique for segmenting WBCs from microscopic blood images based on thresholding segmentation technique and it compares with the most commonly used segmentation technique which is known as color-k-means clustering. The comparison is done based on three well-known measurements, used for evaluating segmentation techniques which are probability random index, variance of information, and global consistency error. Experimental results demonstrate that the proposed thresholding-based segmentation technique provides better results compared to color-k-means clustering technique for segmenting WBCs as well as the time consumption of the proposed technique is less than the color-k-means which are 70.8144 ms and 204.7188 ms, respectively.

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“…Additionally, leukaemia can be divided into lymphoid (which induces lymphocytes) and myeloid (which gives rise to white blood cells (WBCs), red blood cells (RBCs), and platelets) depending on the type of stem cell affection. Consequently, leukaemia can be categorised into four types: ALL, acute myelogenous leukaemia (AML), chronic lymphoblastic leukaemia (CLL), and chronic myelogenous leukaemia (CML) [5, 6]. In general, blood consists of the following components: WBCs, RBCs, platelets, and plasma [4, 7].…”

Section: Introductionmentioning

confidence: 99%

Efficient computer‐aided diagnosis technique for leukaemia cancer detection

Abdulla

2020

IET image process

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Computer‐aided diagnosis (CAD) is a common tool for the detection of diseases, particularly different types of cancers, based on medical images. Digital image processing thus plays a significant role in the processing and analysis of medical images for diseases identification and detection purposes. In this study, an efficient CAD system for the acute lymphoblastic leukaemia (ALL) detection is proposed. The proposed approach entails two phases. In the first phase, the white blood cells (WBCs) are segmented from the microscopic blood image. The second phase involves extracting important features, such as shape and texture features from the segmented cells. Eventually, on the extracted features, Naïve Bayes and k‐nearest neighbour classifier techniques are implemented to identify the segmented cells into normal and abnormal cells. The performance of the proposed approach has been assessed through comprehensive experiments carried out on the well‐known ALL‐IDB data set of microscopic blood images. The experimental results demonstrate the superior performance of the proposed approach over the state‐of‐the‐art in terms of accuracy rate in which achieved 98.7%.

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An Efficient Implementation of Acute Lymphoblastic Leukemia Images segmentation on the FPGA

Cited by 5 publications

References 23 publications

Deep learning-based image annotation for leukocyte segmentation and classification of blood cell morphology

Deep learning-based image annotation for leukocyte segmentation and classification of blood cell morphology

Thresholding-based White Blood Cells Segmentation from Microscopic Blood Images

Efficient computer‐aided diagnosis technique for leukaemia cancer detection

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