Result comparison with other existing models.

<div><p>Lung cancer (LC) is a leading cause of cancer-related fatalities worldwide, underscoring the urgency of early detection for improved patient outcomes. The main objective of this research is to harness the noble strategies of artificial intelligence for identifying and classifying...

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Bibliographic Details
Main Author:	Md. Sabbir Hossain (9958939) (author)
Other Authors:	Niloy Basak (20903279) (author), Md. Aslam Mollah (20903282) (author), Md. Nahiduzzaman (17873875) (author), Mominul Ahsan (17983840) (author), Julfikar Haider (16942678) (author)
Published:	2025
Subjects:	Medicine Cell Biology Cancer Science Policy Space Science Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Information Systems not elsewhere classified suggested approach offered singular value decomposition related fatalities worldwide publicly available dataset performance indicators attained multiclass classification utilizing ml algorithms along mitigating potential errors matthews correlation coefficient convolutional neural networks cohen &# 8217 16 %, respectively prominent distinctive features extract important features improved patient outcomes enhancing model transparency classifying lung cancers clahe ), contrast research offered state prior research gaps voting ensemble approach svd feature extraction ct scan images binary class classification 49 %, 99 extracted features improved interpretability ensemble model enhanced images employing contrast ct scans research lies study introduces reduce dimensionality perfect score noble strategies new benchmark minimal noise mainly focused main objective main contribution lung cancer leading cause later customized impactful insights f1 score explainable ai early detection clinical applications artificial intelligence 100 %.
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Result comparison with other existing models.

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