A Unified Approach to Predict and Understand Acute Myeloid Leukemia Diagnosis
Author:
Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-99-7383-5_18
Reference25 articles.
1. Saleem S, Amin J, Sharif M, Mallah GA, Kadry S, Gandomi AH (2022) Leukemia segmentation and classification: a comprehensive survey. Comput Biol Med 150:106028
2. Dese K et al (2021) Accurate machine-learning-based classification of Leukemia from blood smear images. Clin Lymphoma Myeloma Leuk 21(11):e903–e914
3. Mandal S, Daivajna V, Machine learning based system for automatic detection of leukemia cancer cell. In: 2019 IEEE 16th India Council International Conference (INDICON), Rajkot, India, pp 1-4. https://doi.org/10.1109/INDICON47234.2019.9029034
4. Negm A, Hassan OA, Kandil AH (2018) A decision support system for acute leukaemia classification based on digital microscopic images. Alex Eng J 57(4):2319–2332. https://doi.org/10.1016/j.aej.2017.08.025
5. Mishra SR, Majhi B, Kumar P, Sharma L (2017) Gray level co-occurrence matrix and random forest based acute lymphoblastic leukemia detection. Biomed Signal Process Control 33:272–280. https://doi.org/10.1016/j.bspc.2016.11.021
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