## 4 Discussion  
The global accelerated course of the pandemic necessitates improved and automated platforms in healthcare systems overloaded with mass testing. The interpretation and analysis of RT-PCR fluorescence data require trained personnel, which represents a heavy burden on laboratory staff. Therefore, an AI-assisted RT-PCR detection model represents a valuable system to reduce the workload in healthcare systems and provide a quicker response to the pandemic. In the current study, a model trained with a DCNN was developed as a COVID-19 RT-PCR diagnosis classification system using fluorescence data from patient nasopharyngeal samples. As RT-PCR data are prone to background noise, the global shape of the fluorescent signal should always be examined by an experienced molecular biologist. This image analysis performed in clinical laboratories is particularly suited for deep convolutional networks. The AI-based detection system developed in this study was able to recognize variations in the curve patterns of the PCR graphs and automatize RT-PCR diagnosis of COVID-19. With the proposed model, false-positive results due to unspecific fluorescence are consequently avoided. Figure 8: Comparison matrix of the DCNN models at the 5% significance level by performing t-tests. \begin{table}
\begin{tabular}{c c} \hline \hline
**Model** & **Execution Time (s)** \\ \hline MobileNetV2 & 515 \\ VGG16 & 2030 \\ VGG19 & 2518 \\ ResNet50 & 1656 \\ DenseNet201 & 1497 \\ EfficientNetB7 & 3433 \\ NASNetLarge & 1774 \\ InceptionResNetV2 & 1741 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Total training execution time of the pre-trained models on the PCR COVID-19 dataset. In a previous study by [PERSON] et al. [36], the authors employed a deep learning model, qPCRepNet, which uses a deep convolutional neural network to analyze the fluorescent readings obtained during SARS-CoV-2 RT-PCR in order to improve test specificity. The DCNN model was proposed to detect stochastic variations (i.e., noise) in the fluorescence signal present in the obtained RT-PCRs, which can prevent correct threshold determination and consequently lead to errors in the qualitative interpretation of the results. A high performance of the qPCRepNet was reported in the detection of false positive results [36]. Alternatively, [PERSON] and colleagues described a deep learning model trained with the long short-term memory (LSTM) network, in which raw data of fluorescence values measured in each 40 cycles of SARS-CoV-2 RT-PCR were used. In this study, the authors reported a high negative screening performance of the developed model if various other information such as patient's clinical characteristics, blood test results and chest CT imaging information was available. The models trained with raw RT-PCR data were confirmed to shorten the diagnosis time of RT-PCR [37]. To the best of our knowledge, this is the first study in literature to develop an AI-based detection and classification system for COVID-19 RT-PCR diagnosis using the fluorescent signal and amplification curves. The developed model can be implemented in any clinical laboratory using a web interface by exporting PCR curves from the RT-PCR instruments. Implementation of such AI-driven diagnosis of SARS-CoV-2 will be invaluable for minimizing the time required for the evaluation of the PCR tests and will reduce human intervention in laboratory practice. This study has some limitations. The proposed network is trained to separate the FAM and HEX images based on the shape of fluorescence data and the corresponding Ct values of each class. The training data in the current study were based on the Ct values recommended by the manufacturer of RT-PCR kits used in this study. One limitation of the model may be the use of different RT-PCR kits with varying cutoff Ct values by users in different laboratories, which may cause the failure of the acquired model to separate the classes properly.