# An Efficient One-Dimensional Texture Representation Approach for Lung Disease Diagnosis
## 5 Results  
Our experiment used the Guangzhou and PA datasets to evaluate the proposed approach using several performance metrics: precision, recall, F1 score, and accuracy. In this experiment, the system configuration included a CPU, Intel(R) Core(TM) i7-10700K, a GPU, NVIDIA GeForce RTX 3090, and a memory size of 64 GB. We compared our method with other convolutional neural network (CNN) models using the same datasets and number of layers. In our study, we chose EfficientNet-B0 [54], a type of CNN architecture designed to optimize accuracy and efficiency. It achieves high levels of accuracy while being computationally efficient, making it well suited for use in environments with limited computational resources. The B0 is the baseline model of the EfficientNet family and is the smallest among the larger models. We also evaluated EfficientNet-V2-Small [57], which marks a significant advancement in the design of convolutional neural networks. This model offers an optimal balance between performance and efficiency, achieving an impressive top-1 accuracy of 87.3% on the ImageNet dataset while maintaining a compact size of just 20.33 million parameters. We also evaluated the performance of MobileNet-V3-Small [58]. MobileNetV3-Small is an efficient convolutional neural network developed using a combination of neural architecture search (NAS) and manual optimizations. It reaches a top-1 accuracy of 67.5%  
Figure 6: Feature visualization of Guangzhou dataset and PA dataset.  
training and testing times can increase the efficiency of the model used for a disease-type diagnosis. Table 4 shows the training and testing times for different models using the PA and Guangzhou datasets. The 1 DTR model is the fastest for both datasets, with training times of 38.14 s and 10.85 s on the PA and Guangzhou datasets, respectively. Its testing times are also very quick, at 0.17 and 0.19 s.  
Figure 7: Confusion matrix of each experiment, comparing the performance of 1 DTR with the neural network models (EfficientNet, EfficientNet-V2-Small, and MobileNet-V3-Small). (**a**) 1 DTR Guangzhou dataset confusion matrix. (**b**) EfficientNet Guangzhou dataset confusion matrix. (**c**) EfficientNet-V2-Small Guangzhou dataset confusion matrix. (**d**) MobileNet-V3-Small Guangzhou dataset confusion matrix. (**e**) 1 DTR PA dataset confusion matrix. (**f**) EfficientNet PA dataset confusion matrix. (**g**) EfficientNet-V2-Small PA dataset confusion matrix. (**h**) MobileNet-V3-Small PA dataset confusion matrix.  
In comparison, models such as EfficientNet-B0, EfficientNet-V2, and MobileNet-V3 require significantly more time to train and test, particularly on the PA dataset. Among these, EfficientNet-B0 has the longest training time, taking 7776.78 s on the PA dataset. On the other hand, MobileNet-V3 is the quickest of the neural network models, with training times of 6425.22 s for the PA dataset and 4920.32 s for the Guangzhou dataset.  
According to the results, the 1 DTR significantly outperformed the neural network models in terms of both accuracy and speed of classification. Therefore, using 1 DTR involves lower computational complexity and a reduced processing time.