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Applications of Computational Intelligence and Data Fusion Techniques for Biomedical Images.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Applications of Computational Intelligence and Data Fusion Techniques for Biomedical Images./
Author:	Nambisan, Anand Krishnadas.
Description:	1 online resource (111 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 85-12, Section: B.
Contained By:	Dissertations Abstracts International85-12B.
Subject:	Dermatology. -
Online resource:	click for full text (PQDT)
ISBN:	9798383098950

Applications of Computational Intelligence and Data Fusion Techniques for Biomedical Images.
Nambisan, Anand Krishnadas.

Applications of Computational Intelligence and Data Fusion Techniques for Biomedical Images. - 1 online resource (111 pages)

Source: Dissertations Abstracts International, Volume: 85-12, Section: B.

Thesis (Ph.D.)--Missouri University of Science and Technology, 2024.

Includes bibliographical references

The realm of melanoma diagnosis has been significantly advanced by deep learning (DL) techniques, yet the current approaches are not without limitations, including missed diagnoses and the challenge of interpreting these "black box" models. The research is comprised of three studies, each contributing uniquely towards advancing melanoma detection accuracy and interpretability. The first study focuses on improving the detection of specific dermoscopic structures through a deep learning-based segmentation approach, while the second study builds upon this by employing a fusion technique that combines traditional image features with advanced deep learning models. This method significantly improves melanoma detection, particularly in recall and accuracy. The third study expands the scope by exploring the fusion of deep learning with conventional imaging processing methods, emphasizing the enhancement of diagnostic specificity, and addressing the critical aspect of explainability in AI diagnostics.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798383098950Subjects--Topical Terms:

669082
Dermatology.
Subjects--Index Terms:

Deep learningIndex Terms--Genre/Form:

554714
Electronic books.

Applications of Computational Intelligence and Data Fusion Techniques for Biomedical Images.
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520 $a The realm of melanoma diagnosis has been significantly advanced by deep learning (DL) techniques, yet the current approaches are not without limitations, including missed diagnoses and the challenge of interpreting these "black box" models. The research is comprised of three studies, each contributing uniquely towards advancing melanoma detection accuracy and interpretability. The first study focuses on improving the detection of specific dermoscopic structures through a deep learning-based segmentation approach, while the second study builds upon this by employing a fusion technique that combines traditional image features with advanced deep learning models. This method significantly improves melanoma detection, particularly in recall and accuracy. The third study expands the scope by exploring the fusion of deep learning with conventional imaging processing methods, emphasizing the enhancement of diagnostic specificity, and addressing the critical aspect of explainability in AI diagnostics.
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