Review on Image Segmentation Techniques Incorporated with Machine Learning in the Scrutinization of Leukemic Microscopic Stained Blood Smear Images

Umamaheswari, Duraiswamy; Geetha, S.

doi:10.1007/978-3-030-00665-5_163

Cited by 14 publications

(13 citation statements)

References 71 publications

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“…They came to two significant conclusions at the conclusion of their reviews, the first among one is Hybrid techniques combining machine learning and image processing may improve Leukemia detection. And the second one is, A benchmark dataset is required to iden-tify periodic advancements to the proposed schemes [28]. Thanh et al proposed a comprehensive model based on CNNs for the early screening of acute Leukemia disease.…”

Section: Related Work and Literaturementioning

confidence: 99%

Acute Myeloid Leukemia Multi-classification using Enhanced Few-shot Learning Technique

Venkatesh

Pasupathy

Raja

2022

SCPE

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Acute Myeloid Leukemia (AML) is a form of the condition that is fatal and has a high mortality rate. It is characterised by abnormal cells growing rapidly inside the human body. The conventional method for detecting AML seems to be examining the blood sample manually under a microscope, which is a manual and cumbersome task that also requires well-trained medical expertise for efficient identification. On the other hand, considering medical diagnosis, the capacity to classify medical images faster and accurate is essential. The classification of medical images my currently be accomplished using a range of methodologies including Machine Learning (ML), Deep Learning (DL) and Transfer Learning (RL). While these approaches are effective for large datasets, they can take a while and~not ideal for small datasets. In recent years, advances in Deep Convolutional Neural Networks (DCNN) have made it possible and produce more accurate and promising outcome while processing a~medical image. However, the paradigm that DCNN~use for training includes a large number of annotations in order to prevent overfitting and produce promising results. Obtaining large-scale semantic annotations in clinical operations might be problematic in some cases, particularly biological expertise knowledge is needed. It is also regular occurrence in scenarios where only a small number of annotated classes are accessible in some circumstances. At this context, in order overcome the drawback of traditional approach a framework has been developed which comprises of Enhanced Few-Shot Learning Technique integrated Base Classifier (Feature Encoder)-EFLTBC. The proposed model has built using base classifier and meta-learning block, and it optimized the better results. To diagnose AML, the doctor must count the number of white blood cells and red blood cells and see if there are any abnormal health conditions in that using a microscope. However, obtaining an accurate result takes time and effort. To address these issues, the proposed Novel AML detection model employing is used in this study. Base classifier utilizing ResNet-18 pretrained model and meta learning block has computed using the average feature of every samples. Also, the dataset that we used consisting of three classes includes Normal monocytes, Abnormal monocytes, Lymphocyte and Experimental results outperform various existing deep learning technique with the accuracy of 97%, recall of 96.55% F1-Score of 96.65% and precision of 96.60.

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Section: Related Work and Literaturementioning

confidence: 99%

Acute Myeloid Leukemia Multi-classification using Enhanced Few-shot Learning Technique

Venkatesh

Pasupathy

Raja

2022

SCPE

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“…ey concluded their reviews with two significant findings: (i) Machine-learning-and imageprocessing-based hybrid techniques could offer better results in leukemia detection. (ii) A benchmark dataset is needed to find improvements in the methods proposed from time to time [28].…”

Section: White Blood Cells (Wbcs)mentioning

confidence: 99%

Acute Myeloid Leukemia (AML) Detection Using AlexNet Model

et al. 2021

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Acute Myeloid Leukemia (AML) is a kind of fatal blood cancer with a high death rate caused by abnormal cells’ rapid growth in the human body. The usual method to detect AML is the manual microscopic examination of the blood sample, which is tedious and time-consuming and requires a skilled medical operator for accurate detection. In this work, we proposed an AlexNet-based classification model to detect Acute Myeloid Leukemia (AML) in microscopic blood images and compared its performance with LeNet-5-based model in Precision, Recall, Accuracy, and Quadratic Loss. The experiments are conducted on a dataset of four thousand blood smear samples. The results show that AlexNet was able to identify 88.9% of images correctly with 87.4% precision and 98.58% accuracy, whereas LeNet-5 correctly identified 85.3% of images with 83.6% precision and 96.25% accuracy.

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“…Key steps in DNN-based assessment of bone marrow and peripheral blood smears are cell segmentation, extraction, quantification of cell-specific features, and subsequent cell classification. 30 Especially in leukemia, precise recognition of white blood cells with various segmentation techniques (filtering, enhancement, edge detection, feature extraction, and classification) 31 is crucial for correctly distinguishing between leukemic and non-leukemic cells. [32][33][34] ML can use these techniques to analyze whole slides with automated focusing.…”

Section: Diagnosismentioning

confidence: 99%

Application of machine learning in the management of acute myeloid leukemia: current practice and future prospects

Bornhäuser

Wendt²,

Middeke

2020

Blood Advances

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Machine learning (ML) is rapidly emerging in several fields of cancer research. ML algorithms can deal with vast amounts of medical data and provide a better understanding of malignant disease. Its ability to process information from different diagnostic modalities and functions to predict prognosis and suggest therapeutic strategies indicates that ML is a promising tool for the future management of hematologic malignancies; acute myeloid leukemia (AML) is a model disease of various recent studies. An integration of these ML techniques into various applications in AML management can assure fast and accurate diagnosis as well as precise risk stratification and optimal therapy. Nevertheless, these techniques come with various pitfalls and need a strict regulatory framework to ensure safe use of ML. This comprehensive review highlights and discusses recent advances in ML techniques in the management of AML as a model disease of hematologic neoplasms, enabling researchers and clinicians alike to critically evaluate this upcoming, potentially practice-changing technology.

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Review on Image Segmentation Techniques Incorporated with Machine Learning in the Scrutinization of Leukemic Microscopic Stained Blood Smear Images

Cited by 14 publications

References 71 publications

Acute Myeloid Leukemia Multi-classification using Enhanced Few-shot Learning Technique

Acute Myeloid Leukemia Multi-classification using Enhanced Few-shot Learning Technique

Acute Myeloid Leukemia (AML) Detection Using AlexNet Model

Application of machine learning in the management of acute myeloid leukemia: current practice and future prospects

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