Enhancing Radiology Diagnosis through Convolutional Neural Networks for Computer Vision in Healthcare
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arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866909074715574272 |
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| author | K., Keshav Kumar Narasimham, Dr N V S L |
| author_facet | K., Keshav Kumar Narasimham, Dr N V S L |
| contents | The transformative power of Convolutional Neural Networks (CNNs) in radiology diagnostics is examined in this study, with a focus on interpretability, effectiveness, and ethical issues. With an altered DenseNet architecture, the CNN performs admirably in terms of particularity, sensitivity, as well as accuracy. Its superiority over conventional methods is validated by comparative analyses, which highlight efficiency gains. Nonetheless, interpretability issues highlight the necessity of sophisticated methods in addition to continuous model improvement. Integration issues like interoperability and radiologists' training lead to suggestions for teamwork. Systematic consideration of the ethical implications is carried out, necessitating extensive frameworks. Refinement of architectures, interpretability, alongside ethical considerations need to be prioritized in future work for responsible CNN deployment in radiology diagnostics. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2311_11234 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Enhancing Radiology Diagnosis through Convolutional Neural Networks for Computer Vision in Healthcare K., Keshav Kumar Narasimham, Dr N V S L Image and Video Processing Computer Vision and Pattern Recognition The transformative power of Convolutional Neural Networks (CNNs) in radiology diagnostics is examined in this study, with a focus on interpretability, effectiveness, and ethical issues. With an altered DenseNet architecture, the CNN performs admirably in terms of particularity, sensitivity, as well as accuracy. Its superiority over conventional methods is validated by comparative analyses, which highlight efficiency gains. Nonetheless, interpretability issues highlight the necessity of sophisticated methods in addition to continuous model improvement. Integration issues like interoperability and radiologists' training lead to suggestions for teamwork. Systematic consideration of the ethical implications is carried out, necessitating extensive frameworks. Refinement of architectures, interpretability, alongside ethical considerations need to be prioritized in future work for responsible CNN deployment in radiology diagnostics. |
| title | Enhancing Radiology Diagnosis through Convolutional Neural Networks for Computer Vision in Healthcare |
| topic | Image and Video Processing Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2311.11234 |