# An Efficient One-Dimensional Texture Representation Approach for Lung Disease Diagnosis
## 6 Discussion  
The results show that a 1D representation of the lung texture can provide enough meaningful features without needing a full image to train the FFNN DL for accurate and fast image classification with comparable results against CNN-based models. Moreover, it shows the importance of data manipulation for accurate performance.  
Pneumonia and COVID-19 are communicable and contagious diseases that quickly spread among communities in crowded environments and are leading causes of morbidity and mortality globally. The World Health Organization estimated pneumonia incidence to be over 120 million early patients, with around 10 million severe cases. COVID-19 has affected billions of people since the pandemic started in late 2019, and as of mid-2023, there have been around 760 million confirmed cases, with over 6.9 million deaths worldwide [59]. Poor, inadequate, or delayed diagnosis contributes to their further spread and can lead to more severe outcomes, mismanagement of the infection, and increased transmission of the disease [59; 60]. Additionally, as presented in the introduction and related studies, X-ray image datasets are large and require costly equipment and resources. Therefore, there are more likely more direct methods to handle 1D data as a rule-based model. For example, instead of employing DL, it is possible to utilize a simple algorithm to identify the pixels' range. If the range falls between 0 and 127, it is considered normal; a range of 128 to 255 signifies pneumonia. However, this straightforward approach is not feasible for several reasons. As shown in Figure 7, the overlap between classes makes classifying based on pixel ranges challenging, and individual differences in chest bone thickness can affect pixel values. Thus, we are converting the X-ray vertically instead of pooling to 1D representation descriptive statistics, and the LBP can extract meaningful attributes about the image for the classifiers. Moreover, it significantly decreases the dataset size, which minimizes storage requirements and speeds up the training and testing processes. This reduction does not affect the DL model's performance, facilitating quicker diagnoses and patient outcomes in resource-constrained hospitals and emergency rooms.  
This model can enhance the vital role of technology in advancing healthcare, assisting clinicians globally in detecting respiratory diseases and preventing their spread. While the results of this study show a clear distinction between normal and infected lungs, further investigation is needed into the use of the DL model for more accurate classification. The confusion in classifying bacterial and viral pneumonia attributes indicates the need for ongoing research. This method could be more efficient for preliminary analysis or tasks  
\begin{table}
\begin{tabular}{l l l} \hline \hline  & **Training Time** & **Testing Time** \\ \hline
1 DTR (PA dataset) & 38.14 s & 0.17 s \\ EfficientNet-B0 (PA dataset) & 7776.78 s & 8.56 s \\ EfficientNet-V2 (PA dataset) & 7128.57 s & 7.55 s \\ MobileNet-V3 (PA dataset) & 6425.22 s & 7.24 s \\
1 DTR (Guangzhou dataset) & 10.85 s & 0.19 s \\ EfficientNet-B0 (Guangzhou dataset) & 6251.16 s & 7.02 s \\ EfficientNet-V2 (Guangzhou dataset) & 5393.67 s & 6.20 s \\ MobileNet-V3 (Guangzhou dataset) & 4920.32 s & 5.79 s \\ \hline \hline \end{tabular}
\end{table}
Table 4: Training and testing time of both models using PA and Guangzhou datasets.  
with less critical detailed spatial relationships. However, this model may not be suitable for applications that require object segmentation and masking.