Feature selection results.

<div><p>Manual diagnosis of hematological cancers like leukemia through bone marrow smear analysis is labor-intensive, prone to errors, and highly dependent on expert knowledge. To overcome these limitations, this study introduces a comprehensive deep learning framework enhanced with the...

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Bibliographic Details
Main Author:	Doaa Sami Khafaga (21463870) (author)
Other Authors:	El-Sayed M. El-kenawy (14581088) (author), Faris H. Rizk (21755909) (author), Marwa M. Eid (11251630) (author), Ehsaneh Khodadadi (22146329) (author), Nima Khodadadi (14581091) (author)
Published:	2025
Subjects:	Biotechnology Sociology Hematology Space Science Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified false negative rate effective feature selection driven deep learning deep learning models contributions include developing clinical diagnostics quantifiably average classification error automated cytology technology optimization also results metaheuristic optimization techniques enhancing cnn performance ocoa ), designed increases cnn accuracy ocotillo optimization hyperparameter optimization study introduces significantly reduced sensitivity increasing maximum accuracy innovative bio initial accuracy highly dependent findings highlight expert knowledge critical scenarios continuous version binary variant accurate solution 61 %), 48 %. 24 %. (+ 12 (+ 11
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Feature selection results.

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