Developments in Deep Learning Artificial Neural Network Techniques for Medical Image Analysis and Interpretation
Deep learning has revolutionised medical image analysis, offering the possibility of automated, efficient, and highly accurate diagnostic solutions. This article explores recent developments in deep learning techniques applied to medical imaging, including convolutional neural networks (CNNs) for cl...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-04-01
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| Series: | Diagnostics |
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| Online Access: | https://www.mdpi.com/2075-4418/15/9/1072 |
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| author | Olamilekan Shobayo Reza Saatchi |
| author_facet | Olamilekan Shobayo Reza Saatchi |
| author_sort | Olamilekan Shobayo |
| collection | DOAJ |
| description | Deep learning has revolutionised medical image analysis, offering the possibility of automated, efficient, and highly accurate diagnostic solutions. This article explores recent developments in deep learning techniques applied to medical imaging, including convolutional neural networks (CNNs) for classification and segmentation, recurrent neural networks (RNNs) for temporal analysis, autoencoders for feature extraction, and generative adversarial networks (GANs) for image synthesis and augmentation. Additionally, U-Net models for segmentation, vision transformers (ViTs) for global feature extraction, and hybrid models integrating multiple architectures are explored. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) process were used, and searches on PubMed, Google Scholar, and Scopus databases were conducted. The findings highlight key challenges such as data availability, interpretability, overfitting, and computational requirements. While deep learning has demonstrated significant potential in enhancing diagnostic accuracy across multiple medical imaging modalities—including MRI, CT, US, and X-ray—factors such as model trust, data privacy, and ethical considerations remain ongoing concerns. The study underscores the importance of integrating multimodal data, improving computational efficiency, and advancing explainability to facilitate broader clinical adoption. Future research directions emphasize optimising deep learning models for real-time applications, enhancing interpretability, and integrating deep learning with existing healthcare frameworks for improved patient outcomes. |
| format | Article |
| id | doaj-art-7fc2078b2bb847108985ef3cf7247a28 |
| institution | OA Journals |
| issn | 2075-4418 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diagnostics |
| spelling | doaj-art-7fc2078b2bb847108985ef3cf7247a282025-08-20T01:49:14ZengMDPI AGDiagnostics2075-44182025-04-01159107210.3390/diagnostics15091072Developments in Deep Learning Artificial Neural Network Techniques for Medical Image Analysis and InterpretationOlamilekan Shobayo0Reza Saatchi1School of Engineering and Built Environment, Sheffield Hallam University, Pond Street, Sheffield S1 1WB, UKSchool of Engineering and Built Environment, Sheffield Hallam University, Pond Street, Sheffield S1 1WB, UKDeep learning has revolutionised medical image analysis, offering the possibility of automated, efficient, and highly accurate diagnostic solutions. This article explores recent developments in deep learning techniques applied to medical imaging, including convolutional neural networks (CNNs) for classification and segmentation, recurrent neural networks (RNNs) for temporal analysis, autoencoders for feature extraction, and generative adversarial networks (GANs) for image synthesis and augmentation. Additionally, U-Net models for segmentation, vision transformers (ViTs) for global feature extraction, and hybrid models integrating multiple architectures are explored. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) process were used, and searches on PubMed, Google Scholar, and Scopus databases were conducted. The findings highlight key challenges such as data availability, interpretability, overfitting, and computational requirements. While deep learning has demonstrated significant potential in enhancing diagnostic accuracy across multiple medical imaging modalities—including MRI, CT, US, and X-ray—factors such as model trust, data privacy, and ethical considerations remain ongoing concerns. The study underscores the importance of integrating multimodal data, improving computational efficiency, and advancing explainability to facilitate broader clinical adoption. Future research directions emphasize optimising deep learning models for real-time applications, enhancing interpretability, and integrating deep learning with existing healthcare frameworks for improved patient outcomes.https://www.mdpi.com/2075-4418/15/9/1072artificial intelligenceartificial neural networksmedical image analysisdeep learningimage classification and pattern recognition |
| spellingShingle | Olamilekan Shobayo Reza Saatchi Developments in Deep Learning Artificial Neural Network Techniques for Medical Image Analysis and Interpretation Diagnostics artificial intelligence artificial neural networks medical image analysis deep learning image classification and pattern recognition |
| title | Developments in Deep Learning Artificial Neural Network Techniques for Medical Image Analysis and Interpretation |
| title_full | Developments in Deep Learning Artificial Neural Network Techniques for Medical Image Analysis and Interpretation |
| title_fullStr | Developments in Deep Learning Artificial Neural Network Techniques for Medical Image Analysis and Interpretation |
| title_full_unstemmed | Developments in Deep Learning Artificial Neural Network Techniques for Medical Image Analysis and Interpretation |
| title_short | Developments in Deep Learning Artificial Neural Network Techniques for Medical Image Analysis and Interpretation |
| title_sort | developments in deep learning artificial neural network techniques for medical image analysis and interpretation |
| topic | artificial intelligence artificial neural networks medical image analysis deep learning image classification and pattern recognition |
| url | https://www.mdpi.com/2075-4418/15/9/1072 |
| work_keys_str_mv | AT olamilekanshobayo developmentsindeeplearningartificialneuralnetworktechniquesformedicalimageanalysisandinterpretation AT rezasaatchi developmentsindeeplearningartificialneuralnetworktechniquesformedicalimageanalysisandinterpretation |