A Survey of Soft Computing Approaches in Biomedical Imaging

Area of Science:

• Medical Imaging
• Computer Science
• Artificial Intelligence

Background:

• Medical imaging is crucial for disease diagnosis and treatment.
• Soft computing techniques enhance medical image quality and handle uncertainties.
• Existing research has explored various soft computing approaches for medical applications.

Purpose of the Study:

• To review and discuss soft computing approaches in medical imaging.
• To compare different soft computing methods across various imaging modalities.
• To propose future research strategies based on comparative analysis.

Main Methods:

• Review of medical imaging modalities.
• Short review of soft computing approaches: fuzzy logic, artificial neural network (ANN), genetic algorithm, machine learning (ML), and deep learning (DL).
• Comparative analysis of approaches based on system evaluation parameters.

Main Results:

• Soft computing significantly improves medical image quality and diagnostic accuracy.
• Different soft computing methods show varying performance across imaging modalities.
• Comparative analysis identifies strengths and weaknesses of each approach.

Conclusions:

• Soft computing is vital for advancing medical imaging.
• Further research is needed to optimize specific soft computing techniques for different modalities.
• This work provides a foundation for future development in AI-driven medical imaging.