Improving blood cells classification in peripheral blood smears using enhanced incremental training

Al-qudah, Rabiah; Suen, Ching Y.

doi:10.1016/j.compbiomed.2021.104265

Cited by 13 publications

(5 citation statements)

References 24 publications

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“…The utilization of deep neural networks has significantly improved the precision of platelet classification according to their morphological features, achieving accuracies of up to 98.6%. Al-qudah and Suen demonstrated that through the use of enhanced incremental training techniques, the classification of blood cells in peripheral blood smears could be significantly improved, which supports these high accuracy levels [21].…”

Section: Introduction (Literary Review)mentioning

confidence: 69%

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Implementation of a Base of Rules for Differential Diagnosis of Clinical and Hematological Syndromes Based on Morphological Classification Algorithm

Uvaliуeva,

Ismukhamedova,

Belginova

et al. 2024

sjaitu

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The evolving landscape of modern medicine underscores the growing importance of automating diagnostic processes. This advancement is not merely a convenience but a necessity to harness the full potential of technological progress, aiming to elevate research and clinical outcomes to new heights. Among the innovative strides in this field, the development of diagnostic systems based on morphological classification algorithms stands out. Such systems, rooted in comprehensive rule bases for differential diagnosis, promise to revolutionize the way we approach complex medical conditions. This paper introduces a cutting-edge system that epitomizes this evolution. Designed to harness the power of data analysis, it paves the way for groundbreaking research opportunities. At the heart of this system is a sophisticated set of rules derived from a morphological classification algorithm. This foundation enables the system to perform automated diagnoses of a wide array of clinical and hematological syndromes with unprecedented accuracy. A notable application of this technology is its ability to diagnose anemia by analyzing six distinct blood parameters and further categorize the anemia type based on biochemical criteria. The implications of such diagnostic capabilities are profound. By enabling the systematic collection and analysis of statistical data, the system facilitates in-depth research into the prevalence of diseases across different demographic groups. It aids in identifying disease patterns and supports preventive medicine efforts, potentially shifting the paradigm from treatment to prevention. This study not only highlights the system's capacity for enhancing diagnostic precision but also emphasizes its role as a catalyst for medical research and the improvement of healthcare delivery. The integration of such technologies into the medical field promises to enhance the quality of care, streamline diagnostic processes, and open new avenues for medical research, ultimately contributing to the advancement of global health standards.

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Section: Introduction (Literary Review)mentioning

confidence: 69%

“…1) Rule for determining anemia in chronic diseases (normocytic) ( 21): (21) where MACD represents the overall severity of pathology for anemia in chronic diseases, and Fer denotes the ferritin value obtained from the patient's sample results.…”

Section: Rules For Differential Diagnosis Of Clinical and Hematologic...mentioning

confidence: 99%

Implementation of a Base of Rules for Differential Diagnosis of Clinical and Hematological Syndromes Based on Morphological Classification Algorithm

Uvaliуeva,

Ismukhamedova,

Belginova

et al. 2024

sjaitu

View full text Add to dashboard Cite

show abstract

“…By the systematic analysis of peripheral blood smears for alterations to blood cells, a blood smear test can make an important contribution to the formulation of a diagnosis [14]. Therefore, peripheral blood smears are an important screening method and should always be considered [15].…”

Section: Discussionmentioning

confidence: 99%

Multiple Myeloma in a Patient with Rectal Cancer

Fan¹,

Tao²

2022

IJCM

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Multiple myeloma is characterized by the accumulation of clonal, malignant plasma cells in the bone marrow. Multiple lytic skeletal lesions in some tumor patients with multiple myeloma are easily considered as bone metastases secondary to tumors, resulting in a missed diagnosis of multiple myeloma. Herein, we report a rare case, in which rectal cancer with multiple myeloma was initially misdiagnosed with bone metastases secondary to rectal cancer, due to the symptoms of multiple lytic sketetal lesions, and ignoring the abnormal plasma cells in the peripheral circulating blood smear. The patient was finally diagnosed with coexistence of rectal cancer and multiple myeloma. The case focuses on the importance of the peripheral circulating blood smear detection.

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“…Given the very large numbers of cytopathological images to be processed, diagnosis becomes cumbersome and time consuming for doctors, and diagnostic accuracy is also closely related to experts' experience, fatigue and mood and so on. In view of the facts many researchers have proposed some methods (4)(5)(6)(7)(8)(9)(10)(11)(12)(13) for diagnosis of leukemia. The critical step of which is segmentation.…”

Section: Introductionmentioning

confidence: 99%

A lightweight network based on dual-stream feature fusion and dual-domain attention for white blood cells segmentation

Luo,

Wang,

Zhao

et al. 2023

Front. Oncol.

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IntroductionAccurate white blood cells segmentation from cytopathological images is crucial for evaluating leukemia. However, segmentation is difficult in clinical practice. Given the very large numbers of cytopathological images to be processed, diagnosis becomes cumbersome and time consuming, and diagnostic accuracy is also closely related to experts' experience, fatigue and mood and so on. Besides, fully automatic white blood cells segmentation is challenging for several reasons. There exists cell deformation, blurred cell boundaries, and cell color differences, cells overlapping or adhesion.MethodsThe proposed method improves the feature representation capability of the network while reducing parameters and computational redundancy by utilizing the feature reuse of Ghost module to reconstruct a lightweight backbone network. Additionally, a dual-stream feature fusion network (DFFN) based on the feature pyramid network is designed to enhance detailed information acquisition. Furthermore, a dual-domain attention module (DDAM) is developed to extract global features from both frequency and spatial domains simultaneously, resulting in better cell segmentation performance.ResultsExperimental results on ALL-IDB and BCCD datasets demonstrate that our method outperforms existing instance segmentation networks such as Mask R-CNN, PointRend, MS R-CNN, SOLOv2, and YOLACT with an average precision (AP) of 87.41%, while significantly reducing parameters and computational cost.DiscussionOur method is significantly better than the current state-of-the-art single-stage methods in terms of both the number of parameters and FLOPs, and our method has the best performance among all compared methods. However, the performance of our method is still lower than the two-stage instance segmentation algorithms. in future work, how to design a more lightweight network model while ensuring a good accuracy will become an important problem.

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Improving blood cells classification in peripheral blood smears using enhanced incremental training

Cited by 13 publications

References 24 publications

Implementation of a Base of Rules for Differential Diagnosis of Clinical and Hematological Syndromes Based on Morphological Classification Algorithm

Implementation of a Base of Rules for Differential Diagnosis of Clinical and Hematological Syndromes Based on Morphological Classification Algorithm

Multiple Myeloma in a Patient with Rectal Cancer

A lightweight network based on dual-stream feature fusion and dual-domain attention for white blood cells segmentation

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