A Study of CNN and Transfer Learning in Medical Imaging: Advantages, Challenges, Future Scope

Ahmad Waleed Salehi, Shakir Khan (), Gaurav Gupta (), Bayan Ibrahimm Alabduallah, Abrar Almjally, Hadeel Alsolai, Tamanna Siddiqui and Adel Mellit
Additional contact information
Ahmad Waleed Salehi: Yogananda School of AI, Computers and Data Sciences, Shoolini University, Solan 173212, India
Shakir Khan: College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia
Gaurav Gupta: Yogananda School of AI, Computers and Data Sciences, Shoolini University, Solan 173212, India
Bayan Ibrahimm Alabduallah: Department of Information System, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh 11564, Saudi Arabia
Abrar Almjally: College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia
Hadeel Alsolai: Department of Information System, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh 11564, Saudi Arabia
Tamanna Siddiqui: Department of Computer Science, Aligarh Muslim University, Aligarh 202002, India
Adel Mellit: Faculty of Sciences and Technology, University of Jijel, Jijel 18000, Algeria

Sustainability, 2023, vol. 15, issue 7, 1-28

Abstract: This paper presents a comprehensive study of Convolutional Neural Networks (CNN) and transfer learning in the context of medical imaging. Medical imaging plays a critical role in the diagnosis and treatment of diseases, and CNN-based models have demonstrated significant improvements in image analysis and classification tasks. Transfer learning, which involves reusing pre-trained CNN models, has also shown promise in addressing challenges related to small datasets and limited computational resources. This paper reviews the advantages of CNN and transfer learning in medical imaging, including improved accuracy, reduced time and resource requirements, and the ability to address class imbalances. It also discusses challenges, such as the need for large and diverse datasets, and the limited interpretability of deep learning models. What factors contribute to the success of these networks? How are they fashioned, exactly? What motivated them to build the structures that they did? Finally, the paper presents current and future research directions and opportunities, including the development of specialized architectures and the exploration of new modalities and applications for medical imaging using CNN and transfer learning techniques. Overall, the paper highlights the significant potential of CNN and transfer learning in the field of medical imaging, while also acknowledging the need for continued research and development to overcome existing challenges and limitations.

Keywords: deep learning; transfer learning; medical imaging; CNN; machine learning (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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