Lung Cancer Prognosis Through Standardized Pre-Processing And Multifaceted Data Enhancement: A Deep Learning Approach

Abstract

With high rates of morbidity and mortality, lung cancer continues to be a major problem for world health. A precise prognosis is essential for clinical judgment and patient care. This study provides a unique deep learning method for enhancing the prognosis of lung cancer through multimodal data enhancement and standardized pre-processing. The proposed methodology begins with the comprehensive pre-processing of diverse patient data sources, including medical images, clinical records, and genomic information. Standardization techniques are applied to ensure data consistency and reliability, reducing noise and enhancing the quality of the input data. Furthermore, feature selection and extraction methods are employed to identify the most informative variables for prognostic prediction. To harness the full potential of the integrated data, a deep learning architecture is developed. This architecture combines convolutional neural networks (CNNs) for image analysis, recurrent neural networks (RNNs) for sequential clinical data, and fully connected layers for genomic information. By fusing these diverse data modalities, the model captures intricate patterns and relationships, enabling more accurate prognosis. This research paper introduces a cutting-edge deep learning approach for lung cancer prognosis that leverages standardized pre-processing and multifaceted data enhancement. By integrating medical images, clinical records, and genomic information, our model provides clinicians with a powerful tool for improving patient outcomes through more precise prognostic predictions. This research contributes to the advancement of personalized medicine in lung cancer management, offering new avenues for early intervention and tailored treatment strategies.