The utilization of deep learning in medical imaging for tasks such as segmentation and classification has shown promising results, significantly aiding in diagnostic processes. However, the development of highly accurate models often requires extensive computational resources and large, annotated datasets, which are scarce in the medical field. Transfer learning has emerged as a pivotal technique to mitigate these challenges by leveraging pre-trained models on large datasets and adapting them to specific medical tasks, thereby enhancing model performance with limited data.
This study adopts a fine-tuning approach for transfer learning to address the tasks of segmentation and classification in medical imaging. We utilize pre-trained models as feature extractors and fine-tune them on medical imaging datasets to tailor the models to specific tasks. The methodology is implemented across three distinct applications, each focusing on different aspects of medical image analysis: DeepSeg for precise segmentation of medical images, NeuroXAI for robust classification in a diverse medical dataset, and TransXAI, which integrates transfer learning with explainable AI to provide insights into model decisions.
The fine-tuning methodology demonstrated significant improvements in model performance across all applications. DeepSeg achieved state-of-the-art segmentation accuracy, enhancing the delineation of anatomical structures in medical images. NeuroXAI outperformed baseline models in classification tasks, showcasing the robustness of the ensemble approach in handling diverse medical images. TransXAI not only improved classification accuracy but also provided interpretable insights, facilitating trust and understanding of AI decisions among medical practitioners.
The successful application of transfer learning through fine-tuning across different medical imaging tasks underscores its potential in enhancing model performance, especially when faced with the challenges of limited data and computational resources. The incorporation of explainable AI further demonstrates the feasibility of creating not only accurate but also transparent AI tools in healthcare. These findings advocate for the broader adoption of transfer learning and explainable AI in medical imaging, paving the way for more efficient, accurate, and trustworthy diagnostic tools.