Automatic Detecftion of White Blood Cancer From Blood Cells Using Novel Machine Lerning Techniques

K, Aldrin karunharan.; C, Kanmani Pappa.; Ruby, E. D. Kanmani; Sathyasri, B.; W, Edwin Shanthakumar.

doi:10.1109/icaccs54159.2022.9785023

Cited by 2 publications

(1 citation statement)

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“…Paper [9] highlights the importance of automated detection methods for white blood cell cancer, particularly leukemia, due to its critical nature and the need for accurate and timely diagnosis. Previous studies have explored various image processing techniques, including segmentation and classification algorithms, to differentiate between normal and cancerous cells.…”

Section: Literature Surveymentioning

confidence: 99%

The Enigma of Blood Cancer: Advances in Detection

Jaiswal

2024

IJSREM

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Blood cancer, also called hematological malignancy, is a group of cancers that affect the blood, bone marrow and lymph nodes. Early and accurate detection of blood cancers is essential for effective treatment and improved patient outcomes. In recent time, deep-learning algorithms have become effective tools in medical image analysis and disease detection. The purpose of this paper is to provide a comprehensive overview of the application of various methods in the detection of blood cancer. The beginning of the article describes the different forms of blood cancer, the factors that lead to cancer and the difficulties in detecting them. The article discusses various techniques related to the cancer detection, various machine learning models, deep learning models and mobile nano sensors and their help in detecting blood cancer in the human body. The article ends with a discussion of the future trends and development of the industry. It highlights the importance of different cancer detection methods and how effective these methods are in helping to diagnose blood cancer at an early stage, which would reduce deaths. Keywords: Blood cancer, detection, deep learning, machine learning, medical imaging, early diagnosis, treatment, outcomes, hematological malignancy

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Section: Literature Surveymentioning

confidence: 99%