Enhancing Lung Cancer Detection: Optimizing CNN Architectures through Hyperparameter Tuning

Enhancing Lung Cancer Detection: Optimizing CNN Architectures through Hyperparameter Tuning

CNN architecture; convolutional neural network (CNN); disease detection; hyperparameter tuning; medical image classification; X-ray

This study aimed to compare the performance of various Convolutional Neural Network (CNN) architectures, including LeNet, ResNet, AlexNet, GoogleNet, VGGNet, and the proposed model, in medical image classification for disease detection. The proposed model was developed by adding additional layers and fine-tuning the hyperparameters in the ResNet architecture to enhance its ability to extract complex features. The training and testing processes were conducted using an augmented X-ray image dataset to increase the data diversity. The results indicate that the proposed model achieved the highest testing accuracy of 76.33%, surpassing other models in terms of accuracy, precision, recall, and F1-score. Although there are some limitations in specificity and the Matthews Correlation Coefficient (MCC), the proposed model still demonstrates better generalization ability, with an AUC-ROC score approaching an optimal value. These findings suggest that the proposed model has advantages in medical image classification and holds potential for further development to enhance disease classification accuracy

Sundari Retno Andani1*, Poningsih2, Abdul Karim3

https://jurnal.iaii.or.id/index.php/RESTI/article/view/6357/1127

Informatics Study Program, Master's Program, STIKOM Tunas Bangsa, Pematangsiantar, Indonesia

August 25, 2025

FAJAR BAGUS W

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