Work

Enhancing Lung Cancer Detection: Optimizing CNN Architectures through Hyperparameter Tuning

Year: 2025

Type: article

Abstract: TThis 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.

Source: Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)

DOI: 10.29207/resti.v9i4.6357

Authors: Sundari Retno Andani, Poningsih, Abdul Karim

Institutions: STIKOM Tunas Bangsa, Universitas Labuhanbatu

Language: English

Topic: AI in cancer detection

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $150