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Metaheuristics for Transfer Learning and Model Generalization in Biomedical Imaging
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Author(s): T. S. Poornappriya (Research Scholar, Lincoln University College, Malaysia &), Udhayakumar Pasupathi (Research Scholar, Annai College of Arts and), V. Padmajothi (SRM Institute of Science and Technology, Kattankulathur, India), S. Vijayalakshmi (SRM Institute of Science and Technology, Kattankulathur, India), C. Anuradha (SRM Institute of Science and Technology, Kattankulathur, India), T. Dheepak (Bharathidasan University, Trichiruppalli, India)and M. Subhashini (Srimad Andavan Arts and Science College, Trichy, India)
Copyright: 2026
Pages: 22
Source title:
Metaheuristic Algorithms and Optimizing Neural Networks for Biomedical Image Processing
Source Author(s)/Editor(s): Prasanalakshmi Balaji (King Khalid University, Saudi Arabia), K. Martin Sagayam (Karunya Institute of Technology and Sciences, India), Aditi Sharma (Symbiosis International University, India)and Korhen Cengiz (University of Fujairah, UAE)
DOI: 10.4018/979-8-3373-0523-3.ch011
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Abstract
By enabling automated analysis and interpretation of medical imaging, biomedical image analysis contributes significantly to modern medicine. Medical photos are automatically labeled or categorized according to their content using biometric image classification. Consequently, we present a new hybrid meta-heuristic and deep learning-based fusion model for biomedical picture interpretation in this chapter. A paradigm shift and several notable advancements have resulted from the use of multi-step transition learning to medical picture interpretation. High-level features that may be expertly tailored to the particular needs of the target medical imaging task can be extracted from the source domain using this method. This flexibility is especially helpful in situations when gathering a lot of labeled medical data is time-consuming or difficult.
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