Analysis of Blood Cancer Using Microscopic Image Processing

Kuresan, Harisudha; Satish, J. Sabastian; Shanmugam, Nivash

doi:10.1007/978-981-16-1249-7_38

Cited by 4 publications

(2 citation statements)

References 13 publications

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“…In order to improve distinctiveness, a picture upgrade was carried out, which can improve the quality of a restored image. [10].…”

Section: Modualsmentioning

confidence: 99%

Leukemia detection using Artificial Neural Networks in Images of Human Blood Sample

Mohamed Salih,

Arif,

Sabri

et al. 2024

AJMS

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This article presents a preliminary report that uses minuscule images of blood tests to develop a diagnosis of leukemia. Examining through images is crucial since illnesses can be recognized and examined at an earlier stage using the images. The framework will be centered on leukemia and white blood cell illness. In fact, even the hematologist has trouble organizing the leukemic cells, and manually arranging the platelets takes a long time and is quite loose. In this way, early detection of leukemia recurrence allows the patient to receive the appropriate treatment. In order to address this problem, the framework will make use of the capabilities in small images and examine surface, geometry, shading, and quantifiable investigation modifications. These features' variations will be utilized as the classifier input. has transformed the use of images K proposes that (NN) and agglomeration. Examining a wide range of failure measures and increasing the intricacy of every system, the findings are examined. Utilizing feedforward (NN), image division is accomplished with less noise and a very sluggish conjunction rate. K-means agglomeration and (ANN) are intentionally used in this analysis to create a collection of processes that will work together to produce a much better presentation in (IS). An analysis has been conducted to determine the best rule for (IS).

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“…In order to improve distinctiveness, a picture upgrade was carried out, which can improve the quality of a restored image. [10].…”

Section: Modualsmentioning

confidence: 99%

Leukemia detection using Artificial Neural Networks in Images of Human Blood Sample

Mohamed Salih,

Arif,

Sabri

et al. 2024

AJMS

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“…Methodologies such as liquid biopsy, which includes extracting circulating tumor DNA or other cancer-related components from blood, provide a minimally invasive method but still a painful and discomforting procedure for the patient [6][7][8]. Furthermore, advances in imaging techniques, such as high-resolution imaging and molecular imaging, allow clinicians to view changes in blood cells and tissues in a non-invasive manner, aiding in the early detection of blood cancer [9][10][11]. Utilizing these non-invasive methods, healthcare providers can diagnose blood cancer in its early stages, allowing for timely treatment and improved patient health.…”

Section: Introductionmentioning

confidence: 99%

Improving blood cancer diagnosis through morphological insights using quantitative phase imaging

Khalid,

Javed,

Cabrera

et al. 2024

Optics, Photonics, and Digital Technologies for Imaging Applications VIII

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Blood cancer remains a major global health challenge, emphasizing the critical need of early diagnosis, for effective treatment and improved patient outcomes. Recently, quantitative phase imaging (QPI) based study of cancerous cell morphology, viability and proliferation, attracts the attention of the pathologist and researchers. In this research article, we have introduced customized QPI based imaging tool for investigation of malignant blood cells for the early detection of cancer. The proposed tool enables the measurement of optical path length variations which gives the provision of labelfree, high-resolution imaging of blood cells, allowing for the precise quantification of cellular parameters such as volume, thickness, and dry mass. The proposed low-cost configuration referred as self-referencing QPI system, makes use of the laser beam for generation of the interferograms. Moreover, this technique has the advantage of numerical focusing, and it is not necessary to place the imaging device at the image plane of the magnifying lens. Therefore, efficient autofocusing feature is designed that ensures the efficacious detection, omitting human error and declining the time-consumption. Moreover, the precision of early cancer diagnosis is enhanced through the integration of convolutional neural network (CNN) and QPI technique, which reduces the likelihood of inaccurate imaging. The non-invasive nature of proposed imaging system minimizes patient discomfort and enables real-time monitoring of disease progression. The methodology demonstrates promising results in the early detection of blood cancer and impassive the need of stained sample preparation. This research contributes to the advancement of personalized medicine and underscores the importance of leveraging quantitative phase imaging for early intervention and improved management of blood cancer.

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LeukoCapsNet: a resource-efficient modified CapsNet model to identify leukemia from blood smear images

Dhalla,

Mittal,

Gupta

2023

Neural Comput & Applic

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Analysis of Blood Cancer Using Microscopic Image Processing

Cited by 4 publications

References 13 publications

Leukemia detection using Artificial Neural Networks in Images of Human Blood Sample

Leukemia detection using Artificial Neural Networks in Images of Human Blood Sample

Improving blood cancer diagnosis through morphological insights using quantitative phase imaging

LeukoCapsNet: a resource-efficient modified CapsNet model to identify leukemia from blood smear images

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