| A. Kanavos, G. Vonitsanos, Ph. Mylonas |
| Integrating Convolutional and Recurrent Neural Networks for Enhanced Medical Image Captioning |
| 6th Worldwide Genomics, Neuroscience, Therapeutics & Data Innovation Summit (GeNeDiS 2024), 17–20 October 2024, Athens, Greece |
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ABSTRACT
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| The rapid expansion of digital medical imaging technologies demands advanced tools for efficient and accurate image analysis. This research introduces a novel approach to medical image captioning, integrating Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) to enhance the automatic generation of descriptive text for medical images. Our proposed model exploits the robust feature extraction capabilities of CNNs alongside the advanced sequential data processing of RNNs. We incorporate an attentio n mechanism that selectively focuses on diagnostically significant areas within images, thereby improving the relevance and accuracy of the generated captions. The effectiveness of our model was validated using an extensive set of evaluation metrics, including BLEU scores for linguistic quality and traditional classification metrics for accuracy. Results indicate that our model significantly outperforms existing systems in syntactic coherence and semantic accuracy, making it a valuable tool for aiding clinical decision-making and enhancing medical documentation.
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| 17 October , 2024 |
| A. Kanavos, G. Vonitsanos, Ph. Mylonas, "Integrating Convolutional and Recurrent Neural Networks for Enhanced Medical Image Captioning", 6th Worldwide Genomics, Neuroscience, Therapeutics & Data Innovation Summit (GeNeDiS 2024), 17–20 October 2024, Athens, Greece |
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