Classification of biomedical lung cancer images using optimized binary bat technique by constructing oblique decision trees

Due to imbalanced data values and high-dimensional features of lung cancer from CT scans images creates significant challenges in clinical research. The improper classification of these images leads towards higher complexity in classification process. These critical issues compromise the extraction...

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Veröffentlicht in:	Scientific reports Jg. 15; H. 1; S. 18954 - 16
Hauptverfasser:	Aswal, Shobha, Ahuja, Neelu Jyothi, Mehra, Ritika
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London Nature Publishing Group UK 29.05.2025 Nature Publishing Group Nature Portfolio
Schlagworte:	631/114 631/114/1564 Algorithms Binary Bat Swarm Algorithm Biomedical data Cancer Classification Decision making Decision Trees Humanities and Social Sciences Humans Image Processing, Computer-Assisted - methods Imbalanced data Lung cancer Lung Neoplasms - classification Lung Neoplasms - diagnostic imaging Machine Learning multidisciplinary Oblique decision tree Science Science (multidisciplinary) Tomography, X-Ray Computed - methods Imbalanced data Binary Bat Swarm Algorithm Oblique decision tree Lung cancer Biomedical data
ISSN:	2045-2322, 2045-2322
Online-Zugang:	Volltext
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