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Deep learning systems are being developed to generate medical reports automatically to reduce doctor workload.

This review provides a systematic and comprehensive analysis of how deep learning models translate visual content into human language, with a particular focus on both general and medical applications. 🔬 Core Components of the Review

“Modern deep learning-based approaches have supplanted traditional approaches in image captioning, leading to more efficient and sophisticated models.” ScienceDirect.com 126287

The identifier refers to the specific article index for a prominent scientific review titled "Deep image captioning: A review of methods, trends and future challenges" , published in the journal Neurocomputing (Volume 546, August 2023).

Metrics like BLEU and ROUGE are used to measure accuracy, but they sometimes struggle to capture the full semantic meaning or clinical relevance of a caption. Deep learning systems are being developed to generate

Using attention mechanisms to identify the most relevant parts of an image for a specific description.

Translating those visual features into coherent text using architectures like RNNs, LSTMs, and Transformers. 🏥 Focus on Medical Report Generation Metrics like BLEU and ROUGE are used to

“Despite the great progress made by existing deep generation methods, it is still inadequate in (1) insufficient consideration of the visual-pathological gap and (2) weak evaluation of clinical language style.” National Institutes of Health (.gov) · 4 months ago