Medical image analysis using deep learning algorithms (DLA)

Dafina Xhako; Niko Hyka; Elda Spahiu; Suela Hoxhaj; Dafina Xhako; Niko Hyka; Elda Spahiu; Suela Hoxhaj

doi:10.3934/biophy.2025008

AIMS Biophysics

2025, Volume 12, Issue 2: 121-143. doi: 10.3934/biophy.2025008

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Research article

Medical image analysis using deep learning algorithms (DLA)

1.
Department of Physics Engineering, Faculty of Mathematical Engineering and Physical Engineering, Polytechnic University of Tirana, Tirana, Albania
2.
Department of Diagnostic, Faculty of Medical Technical Sciences, University of Medicine, Tirana, Albania
3.
Institute of Applied Nuclear Physics, Tirana, Albania
4.
Department of Physics Engineering, Faculty of Mathematical Engineering and Physical Engineering, Polytechnic University of Tirana, Tirana, Albania

Received: 15 January 2025 Revised: 28 February 2025 Accepted: 12 March 2025 Published: 24 March 2025

Deep Learning Algorithms (DLAs) have emerged as transformative tools in medical image analysis, offering unprecedented accuracy and efficiency in diagnostic tasks. We explored the state-of-the-art applications of DLAs in medical imaging, focusing on their role in disease detection, segmentation, workflow automation, and multi-modality data integration. Key architectures such as Convolutional Neural Networks (CNNs), U-Net, and Vision Transformers are highlighted, alongside their tailored applications in healthcare. Additionally, Mamba networks have shown significant promise in medical imaging by leveraging their advanced memory-efficient architecture for high-dimensional data processing. These networks excel in real-time analysis, improving the speed and accuracy of complex imaging tasks such as tumor detection and organ segmentation. The adaptability and computational efficiency of Mamba networks position them as a strong alternative to traditional deep learning architectures in the field of medical imaging. DLAs have consistently demonstrated superior performance compared to radiologists in various diagnostic tasks, such as breast cancer detection and brain tumor segmentation, with higher accuracy and efficiency. Despite these advancements, challenges such as limited data availability, ethical concerns, interpretability issues, and integration hurdles persist. Addressing these barriers is crucial to unlocking the full potential of DLAs and enabling their seamless integration into clinical workflows, ultimately enhancing patient care and diagnostic precision.
- deep learning algorithms,
- medical imaging,
- diagnostic accuracy,
- convolutional neural mamba,
- networks,
- U-net,
- vision transformers,
- workflow automation,
- AI in healthcare
Citation: Dafina Xhako, Niko Hyka, Elda Spahiu, Suela Hoxhaj. Medical image analysis using deep learning algorithms (DLA)[J]. AIMS Biophysics, 2025, 12(2): 121-143. doi: 10.3934/biophy.2025008

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Abstract

Deep Learning Algorithms (DLAs) have emerged as transformative tools in medical image analysis, offering unprecedented accuracy and efficiency in diagnostic tasks. We explored the state-of-the-art applications of DLAs in medical imaging, focusing on their role in disease detection, segmentation, workflow automation, and multi-modality data integration. Key architectures such as Convolutional Neural Networks (CNNs), U-Net, and Vision Transformers are highlighted, alongside their tailored applications in healthcare. Additionally, Mamba networks have shown significant promise in medical imaging by leveraging their advanced memory-efficient architecture for high-dimensional data processing. These networks excel in real-time analysis, improving the speed and accuracy of complex imaging tasks such as tumor detection and organ segmentation. The adaptability and computational efficiency of Mamba networks position them as a strong alternative to traditional deep learning architectures in the field of medical imaging. DLAs have consistently demonstrated superior performance compared to radiologists in various diagnostic tasks, such as breast cancer detection and brain tumor segmentation, with higher accuracy and efficiency. Despite these advancements, challenges such as limited data availability, ethical concerns, interpretability issues, and integration hurdles persist. Addressing these barriers is crucial to unlocking the full potential of DLAs and enabling their seamless integration into clinical workflows, ultimately enhancing patient care and diagnostic precision.

Acknowledgments

The authors would like to thank the National Agency for Scientific Research and Innovation (NASRI) in Albania, EFOMP, READ (Research Expertise from Academic Diaspora), AAMP and Department of Physical Engineering, in Polytechnic University of Tirana.

Conflict of interest

The authors declare no conflicts of interest.

Author contributions

Dafina Xhako conceived and designed the study, supervised the data analysis, and contributed to the writing and revision of the manuscript. Niko Hyka contributed to the development of deep learning models, conducted data analysis, and assisted in manuscript preparation. Elda Spahiu assisted in the dataset collection, preprocessing, and validation of the deep learning models. Suela Hoxhaj provided expertise in medical imaging and contributed to the analysis and interpretation of results. All authors contributed to the final manuscript and approved its submission.

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