# Multilabel Classification of Radiology Image Concepts Using Deep Learning
## 1 Introduction
### The KERP Framework  
The generation of comprehensive and semantically coherent descriptions for medical images poses significant challenges. This process requires bridging the gap between visual perception and linguistic representation, incorporating domain-specific medical knowledge, and ensuring the production of accurate and clinically meaningful descriptions. Addressing these complexities, a novel knowledge-driven approach named Encode, Retrieve, and Paraphrase (KERP) has been proposed. This method integrates traditional data-driven  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **BLEU-1** & **BLEU-2** & **BLEU-3** & **BLEU-4** \\ \hline IU X-Ray & 0.517 & 0.386 & 0.306 & 0.247 \\ PEIR Gross & 0.300 & 0.218 & 0.165 & 0.113 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Performance outcomes as reported in [10].  
retrieval mechanisms with modern knowledge-based modeling to facilitate the generation of precise and context-aware medical reports [13].  
The KERP framework decomposes the task of medical report generation into two sequential phases: initially, the creation of a structured graph representing medical abnormalities detected in the visual input, followed by the generation of corresponding natural language descriptions. Specifically, the Encode module transforms extracted visual features into an organized abnormality graph by leveraging prior medical knowledge. Subsequently, the Retrieve module accesses relevant textual templates associated with the abnormalities identified in the graph. Finally, the Paraphrase module refines the retrieved templates into customized, case-specific medical narratives.  
The essential of KERP is a projected generic application unit--Chart Transformer (GTR) that animatedly converts high-level semantics between graph-structured statistics of multiple domains such as knowledge charts, pictures, and arrangements. The Chart Transformer provides a versatile mechanism for dynamically translating between different semantic spaces, enabling effective interaction between visual, graphical, and textual representations.  
The effectiveness of the KERP approach was validated on two benchmark datasets: IU X-Ray and CX-CHR. The IU X-Ray dataset contains chest radiographs paired with detailed radiology reports, while CX-CHR comprises a private collection of chest x-rays with corresponding clinical descriptions. This research [14] uses BLEU-n to evaluate the performance of abnormality and disease arrangement. Table 2 shows experimental results [10].  
The empirical findings demonstrate that KERP achieves state-of-the-art results on both datasets, with substantial improvements observed in terms of abnormality prediction accuracy, dynamic knowledge graph construction, and the generation of contextually accurate textual descriptions. The fundamental difference between our research and this is that it only focuses on using the chest x-ray. In contrast, there is a large diversity in the dataset we are maximizing in our research.  
Therefore, while KERP represents a notable advancement in the automatic generation of radiological reports from chest x-rays, the broader and more diverse scope addressed in the current study introduces additional challenges that necessitate further methodological innovations.