Performance Comparison of VGG16, VGG19 and Alexnet Pre-Trained Transfer Learning Architecture Models in the Convolutional Neural Network Algorithm in Classification of Lung Disease - Harahap, Fahri Aulia Alfarisi (Universitas Negeri Medan, 2024)
This study aims to comprehend the performance of transfer learning architectures (VGG16, VGG19, and Alexnet) in a Convolutional Neural Network for classifying lung diseases. Another objective is to determine the most superior transfer learning approach in this classification scenario. The dataset consists of 5 classes: normal lungs, pneumonia, bronchopneumonia, tuberculosis, and bronchitis. The data was sourced from Sinar Husni Deli Serdang Hospital through the radiology laboratory. The dataset was divided 80:20 for training and testing, with hyperparameters including a batch size of 32, 50 epochs, and optimization using Adaptive Momentum Optimization with a learning rate of 0.001. The research findings reveal that the VGG19 transfer learning architecture achieves the best performance with an accuracy of 59.17%, precision of 62%, recall of 59.2%, and an f-1 score of 58.8%. VGG16 ranks second with an accuracy of 55.83%, precision of 58%, recall of 55.8%, and an f-1 score of 55.2%. Alexnet has an accuracy of 49.17%, precision of 53.2%, recall of 49.2%, and an f-1 score of 50.6%. In an external test with 50 data points, VGG16 attains an accuracy of 54%, VGG19 scores 42%, and Alexnet records 46%. These models perform better in classifying normal lungs and tuberculosis compared to pneumonia, bronchopneumonia, and bronchitis. Analysis of lung image data demonstrates that homogeneity of RGB pixel values within a class supports transfer learning performance in classification. Conversely, heterogeneity in RGB pixel values can diminish the evaluation of that class.