Blood Cancer Detection Using Improved Machine Learning Algorithm

Dharani, N. P.; Sujatha, G.; Rani, R.

doi:10.1109/iccpct58313.2023.10245375

Cited by 2 publications

(2 citation statements)

References 13 publications

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“…The literature survey conducted by Dharani et al (2023) in Paper [2] emphasizes the growing need for effective blood cancer detection methods due to the increasing prevalence of the disease. They stress the importance of early diagnosis in mitigating the impact of blood cancer on patients' health.…”

Section: Literature Surveymentioning

confidence: 99%

“…Building on this context, the proposed methodology outlined in Paper [2] integrates machine learning techniques with advanced image processing methods to address the shortcomings of traditional diagnostic approaches. By leveraging algorithms like Effective Fuzzy C Means (EFCM) and Iterative Morphological Process (IMP), the authors aim to enhance the accuracy and efficiency of blood cancer detection.…”

Section: Literature Surveymentioning

confidence: 99%

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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%

Section: Literature Surveymentioning

confidence: 99%