Transfer Learning for Cancer Diagnosis in Medical Images: A Compendious Study
Abstract In today’s world, cancer stands out as one of the most perilous diseases, caused by the uncontrolled proliferation of cells within the human body. Early detection is paramount to ensuring that patients receive the necessary medical intervention in a timely manner. Recently, deep learning te...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Springer
2025-03-01
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| Series: | International Journal of Computational Intelligence Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s44196-025-00772-0 |
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| Summary: | Abstract In today’s world, cancer stands out as one of the most perilous diseases, caused by the uncontrolled proliferation of cells within the human body. Early detection is paramount to ensuring that patients receive the necessary medical intervention in a timely manner. Recently, deep learning techniques, particularly convolutional neural networks, have proven to be incredibly effective in developing computer-aided diagnosis systems due to their remarkable accuracy in analyzing medical images. However, the process of training these neural networks from scratch is often complex and requires significant computational resources. Transfer learning has emerged as a powerful solution to overcome this challenge. This study examines the fundamental concepts of machine learning and deep learning-based computer-aided diagnostic systems. It underscores the significant role of transfer learning in enhancing diagnostic accuracy. It also illustrates the various transfer learning models employed to diagnose various cancer forms, including skin cancer, brain tumors, breast cancer, lung cancer, leukemia, prostate cancer, bladder cancer, and cervical cancer. This paper summarizes 151 studies conducted in recent years. In the end, the article offers a thorough discussion of the research findings, overall conclusions, and directions for future work. |
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| ISSN: | 1875-6883 |