Transfer Learning for Early Diagnosis of Rare Diseases Using Medical Imaging
Abstract
Diagnosing rare diseases is a challenging task due to the limited availability of labeled medical imaging data and the complexity of disease patterns. This paper proposes a transfer learning approach to address this issue, leveraging pre-trained deep learning models to improve diagnostic accuracy. The methodology involves fine-tuning models on small datasets specific to rare diseases, significantly reducing the need for extensive labeled data. Experiments on rare disease imaging datasets, such as rare cancer types and genetic disorders, demonstrate the effectiveness of transfer learning in achieving high accuracy with minimal training samples. This study highlights the potential of transfer learning to accelerate the early diagnosis of rare diseases, enabling timely interventions and improving patient outcomes.
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