| _version_ | 1866901939667599360 |
|---|---|
| author | R.Y.Mamajanov R.Suyunova |
| author_facet | R.Y.Mamajanov R.Suyunova |
| contents | <p><span lang="EN-US">This article scientifically analyzes the theoretical foundations, algorithmic approaches, and practical application areas of Machine Learning and Artificial Intelligence technologies in the process of medical image analysis (radiography, CT, MRI, ultrasound, etc.). The role of Convolutional Neural Networks (CNN), Deep Learning methods, as well as segmentation and classification algorithms in improving diagnostic accuracy is substantiated. In addition, the integration of Artificial Intelligence technologies into clinical decision support systems, along with their advantages and existing challenges, is examined.</span></p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_18733689 |
| institution | Zenodo |
| language | |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | TIBBIY TASVIRLARNI TAHLIL QILISHDA MASHINAVIY O'RGANISH VA SUN'IY INTELLEKTNING ROLI R.Y.Mamajanov R.Suyunova medical image, machine learning, artificial intelligence, deep learning, segmentation, classification, CNN, diagnostics. <p><span lang="EN-US">This article scientifically analyzes the theoretical foundations, algorithmic approaches, and practical application areas of Machine Learning and Artificial Intelligence technologies in the process of medical image analysis (radiography, CT, MRI, ultrasound, etc.). The role of Convolutional Neural Networks (CNN), Deep Learning methods, as well as segmentation and classification algorithms in improving diagnostic accuracy is substantiated. In addition, the integration of Artificial Intelligence technologies into clinical decision support systems, along with their advantages and existing challenges, is examined.</span></p> |
| title | TIBBIY TASVIRLARNI TAHLIL QILISHDA MASHINAVIY O'RGANISH VA SUN'IY INTELLEKTNING ROLI |
| topic | medical image, machine learning, artificial intelligence, deep learning, segmentation, classification, CNN, diagnostics. |
| url | https://doi.org/10.5281/zenodo.18733689 |