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Multimodal Learning for Lung Cancer Diagnosis and Management: A Deep Learning Pipeline for Classification, TNM Staging, and Treatment Protocol Generation
| Nome: | Descrição: | Tamanho: | Formato: | |
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| 7.23 MB | Adobe PDF |
Orientador(es)
Resumo(s)
Accurate lung cancer diagnosis and staging are critical for personalized treatment and
improved patient outcomes. Traditional diagnostic methods, such as manual image
examination, are prone to variability and inefficiency. Recent advancements in Deep Learning
(DL) have demonstrated potential in automating lung cancer classification and staging,
thereby enhancing diagnostic accuracy and efficiency. However, existing solutions often
address only isolated aspects of the diagnostic process, such as tumor detection, without
offering a unified system for multi-target classification that integrates clinical tools for both
clinicians and patients. This study presents a unified framework for lung cancer analysis that
combines medical imaging, clinical data, and Large Language Models (LLMs) to support three
key tasks: tumor type classification, TNM staging, and automated treatment protocol
recommendation. Image-based classification was performed using YOLOv8n, trained on two
CT datasets, achieving a maximum mean average precision (mAP50) of 0.418 and an F1 score
of 0.44. TNM staging was addressed through a multimodal classifier, combining the ResNet50
model with a multilayer perceptron, which fused imaging and demographic inputs. This
approach yielded an average F1 score of 0.389, with the M component showing the strongest
performance. For treatment generation, a Retrieval-Augmented Generation (RAG) approach
was employed, combining clinical prompts with relevant documents to produce personalized
protocols using the Gemini 2.0 Flash LLM. The best-performing configuration achieved a stage
match of 0.54 and a BERTScore of 0.83, along with high contextual fidelity across RAGAS
metrics (Faithfulness: 0.68, Answer Relevancy: 0.81, Context Precision: 0.99, and Context
Recall: 0.93). This framework demonstrates the potential to support both diagnosis and
treatment planning within a single integrated system, contributing to more personalized and
effective clinical decision-making in oncology.
Descrição
Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data Science
Palavras-chave
Deep Learning (DL) Large Language Models (LLMs) Lung Cancer Retrieval-Augmented Generation (RAG) Treatment Recommendation