Innovative AI-driven Lung Cancer Diagnosis Unveiling a Novel Neural Network Model for Enhanced Predictive Accuracy

Esraa Raheem Alzaidi

doi:10.32628/IJSRST2411596

Authors

Esraa Raheem Alzaidi College of Science, University of Al-Qadisiyah, Al-Qadisiyah, Iraq Author

DOI:

https://doi.org/10.32628/IJSRST2411596

Keywords:

Lung Cancer, Machine Learning, Deep Learning, Medical Treatment, Multimedia Fusion, Multi- Techniques Integration, Prediction Improvement

Abstract

With the spread of lung cancer and its associated diseases, it has become necessary to develop new visions and strategies to reduce this phenomenon, especially if we know that lung cancer is the beginning of the end. When this disease is diagnosed early and predicted, this phenomenon can be reduced and contribute to improving the patient’s journey. In light of the development of computing sciences, programming and machine learning techniques, creating models that can predict lung cancer has become an easy matter, but effort is still required to develop these models and make their results more accurate and rapid, as time has become an important factor in the lung cancer patient's journey, and through this study, which aims to develop neural network models to predict lung cancer diseases by integrating artificial intelligence techniques to predict lung cancer, this process is called "Multi-T techniques Integration" or "Multimedia fusion" through the methodology of description, analysis, comparison methodology, and scientific methodology, it was developed A model of neural networks to predict lung cancer diseases by integrating deep learning techniques for image analysis, machine learning techniques for data analysis, and medical treatment techniques using sentiment analysis. The results indicated an improvement in accuracy by a rate ranging between 8% to 15%, and an improvement in prediction by 10% to 20%. %, recall improved by a rate ranging from 10% to 15%, and an improvement in the F1-SCORE rate ranging from 10% to 14%. This makes the way open towards making more efforts to develop models for predicting diseases in general and predicting lung cancer in particular.

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