ClinX ‐Net: Multimodal Visual–Language Feature Fusion for Chest X‐Ray Report Generation

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Automated chest X‐ray report generation has the potential to improve reporting efficiency and reduce variability in radiological documentation. However, existing methods remain challenged by subtle abnormality description, long‐range textual consistency, and effective integration of clinical information with visual findings. To address these limitations, we propose ClinX‐Net, a multimodal framework for chest X‐ray report generation that combines hierarchical visual representation learning, disease‐aware semantic encoding, and language‐model‐based text generation. The visual branch employs ConvNeXt to extract multilevel chest radiograph features, which are integrated through multiscale fusion to capture global anatomical structures and localized pathological patterns. Disease label representations are incorporated to provide additional semantic guidance, while a BioBERT encoder captures clinical context from radiology‐related text. The resulting visual, disease‐semantic, and clinical representations are fused to condition a T5‐based decoder for report generation. Experiments were conducted on the publicly available IU X‐Ray and MIMIC‐CXR datasets. On IU X‐Ray, ClinX‐Net achieved a BLEU‐4 of 0.199, ROUGE‐L score of 0.454 and a METEOR score of 0.222, while on MIMIC‐CXR, it achieved BLEU‐4, ROUGE‐L, and METEOR scores of 0.122, 0.296, and 0.174, respectively. Additional semantic and clinical evaluations using BERTScore and CheXbert demonstrate improved contextual similarity and preservation of clinically relevant observations in generated reports. Qualitative analysis further shows improved anatomical descriptions and better localization of reported findings. These results indicate that integrating hierarchical visual features, disease‐aware semantic representations, and clinical context improves the quality and clinical relevance of automated chest X‐ray report generation.