Özet:

Abstract The incidence of lung cancer has been increasing exponentially in recent years due to hazardous consumption habits and environmental factors. While there are ongoing comprehensive research efforts in the field, the accuracy and efficiency of lung cancer detection remain a challenge. To address this, this study proposes a multi-view aspect model using digital image processing techniques for lung cancer research. The model utilizes Convolutional Neural Networks (CNN) to categorize different types of lung cancer, leveraging the power of image classification capabilities. By employing CNNs, the model aims to enhance the diagnostic accuracy in lung cancer detection. To evaluate the model's performance, several metrics are used, including Matthew's correlation coefficient, Cohen's Kappa score, and log loss. Matthew's correlation coefficient measures the correlation between predicted and actual classifications, providing insights into the overall performance of the model. Cohen's Kappa score assesses the agreement beyond chance between predicted and actual classifications. The log loss metric measures the accuracy of the model's probability estimates. By incorporating these evaluation metrics, this research aims to provide a comprehensive assessment of the proposed multi-view aspect model for lung cancer diagnosis. The goal is to improve the accuracy and efficiency of lung cancer detection, enabling earlier interventions and better patient outcomes.

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