| Leveraging Pre-Trained Convolutional Neural Networks for Medical Image Analysis through Transfer Learning. |
| کد مقاله : 1027-NAEC |
| نویسندگان |
|
Muhammad Khalil Ali * Department of computer engineering, Faculty of technology and engineering Ahlul Bayt
International university Tehran Iran |
| چکیده مقاله |
| Medical image analysis is a critical task in healthcare, requiring accurate and efficient diagnosis. Deep learning techniques, particularly convolutional neural networks (CNNs), have shown promising results in medical image analysis. However, training CNNs from scratch can be computationally expensive and require large amounts of labelled data. Transfer learning offers a solution to this problem by leveraging pre-trained CNNs. This paper provides an overview of transfer learning in medical image analysis, focusing on the benefits and challenges of using pre-trained CNNs. We discuss the applications of transfer learning in medical image analysis, including X-ray image analysis and MRI image analysis. The paper also highlights the limitations and future directions of transfer learning in medical image analysis. Additionally, we explore the potential of transfer learning in improving the accuracy and efficiency of medical image analysis, and discuss the importance of developing large-scale medical image datasets for training and validation. Furthermore, we examine the role of transfer learning in enabling the development of more accurate and reliable medical image analysis models. |
| کلیدواژه ها |
| Transfer Learning, Convolutional Neural Networks, Medical Image Analysis, Deep Learning, X-ray Image Analysis |
| وضعیت: پذیرفته شده |
