ROC-AUC curve obtained by ML-IGPA (All classifiers).

ROC-AUC curve obtained by ML-IGPA (All classifiers).

<p>ROC-AUC curve obtained by ML-IGPA (All classifiers).</p>

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Bibliographic Details
Main Author:	Anwar Hossain Efat (19942283) (author)
Other Authors:	S. M. Mahedy Hasan (19942286) (author), Md. Palash Uddin (19139222) (author), Md. Al Mamun (12800084) (author)
Published:	2024
Subjects:	Immunology Cancer Computational Biology Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified ham1000 dataset demonstrates dominance dispersion remains convolutional neural network approach achieves 94 achieving desired outcomes abnormal cell growth level ensemble approach identify responsible regions facilitating early diagnosis including skin diseases customized transfer learning approach &# 8220 igpa ),&# 8221 determining optimal weights model &# 8217 &# 8220 ensemble learning specific regions early detection skin cancer skin abnormalities machine learning deep learning triple attention surpassing state specifically involves primary objective novel method multiple levels integral component exact focus enhance explainability empirical evaluation el ). currently lacking current studies art methods affect structure
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Summary:	<p>ROC-AUC curve obtained by ML-IGPA (All classifiers).</p>

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