# PneumoNet: Artificial Intelligence Assistance for Pneumonia Detection on X-Rays
## 3 Pneumonet Framework
### Pneumonia Classification Module
However, a limitation is the potential lack of methodological consistency in hyperparameters, preprocessing, and data partitioning among the studies presented in Table 3, which undermines fair horizontal comparability. PneumoNet employs a well-defined pipeline, as described in the article; in contrast, cited studies often provide incomplete details, with some (e.g., [19; 25]) omitting preprocessing specifics or using different split ratios, optimisers, or augmentation strategies, potentially inflating or deflating reported metrics relative to PCM's performance. The comparative analysis revealed that PCM achieved the highest accuracy of 98.70%, outperforming models previously proposed in the literature. The superior performance of PCM can be attributed to several key factors. First, the model employs a custom-tailored CNN architecture designed specifically to handle the complexities of medical imaging data, such as variations in contrast, noise, and anatomical structures. Second, the implementation \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Study** & **Method** & **Dataset Train and Test** & **Results** \\ \hline
[26] & AlexNet, GoogLeNet and ResNet & Chest X-ray14 & ACC = 90.70\% \\  & Dataset \#1 [8] & & \\
[19] & Deep learning model using VGG16 & Dataset \#1 [8] & ACC = 95.40\% \\
[27] & HOG + CNN & Dataset \#1 [8] & ACC = 96.70\% \\  & Dataset \#2 [9] & & \\
[25] & VGG-16 & Dataset \#2 [9] & ACC = 87.50\% \\
[28] & Custom trained Sequential CNN Arch. & Dataset \#2 [9] & ACC = 90.20\% \\
[29] & Generated Models & Dataset \#2 [9] & ACC = 83.30\% \\
[30] & VGG-16 & Dataset \#2 [9] & ACC = 96.40\% \\
[20] & MobileNet & Dataset \#2 [9] & ACC = 94.23\% \\
[31] & Comb. Inceptionv3 and Logistic Regression & Dataset \#3 [10] & \\
[32] & EL Approach & & \\
[33] & Quaternion-customised DNN Architecture & Dataset \#3 [10] & ACC = 94.53\% \\
[34] & DenseNet169 & Dataset \#3 [10] & ACC = 95.72\% \\
[35] & CNN + Modified Dropout Model & Dataset \#3 [10] & ACC = 97.20\% \\
[36] & Layer-wise Relevance Propagation (LRP) & Dataset \#3 [10] & ACC = 91.00\% \\  & Datasets \#1, 2, and 3 [8; 9; 10] & \\
[37] & Modified AlexNet & Dataset \#1, 2, and 3 [8; 9; 10] & ACC = 93.42\% \\  & & Dataset \#1 [8] & ACC = 96.70\% \\  & & AUC = 98.39\% \\  & & Dataset \#2 [9] & ACC = 98.70\% \\ PCM & PCM: AlexNet backbone + CNN + BN + FC & & AUC = 99.70\% \\  & Dataset \#3 [10] & ACC = 97.70\% \\  & & AUC = 98.04\% \\  & Dataset \#1, 2, and 3 & ACC = 98.70\% \\  & [8; 9; 10] & AUC = 99.70\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results of the pneumonia classification module of PneumoNet when compared with studies using deep learning to detect pneumonia on X-rays and the results obtained with different datasets. PCM is presented at the bottom of the table, and in the column “Dataset Train & Test”, it is shown the dataset used in the training and testing. of data augmentation techniques ensured the model was resilient to overfitting and capable of generalising effectively across diverse patient demographics and imaging conditions. Third, the inclusion of fine-tuned hyperparameter settings and regularisation strategies further enhanced the prototype's ability to achieve consistent performance. Concerning Dataset #3 [10], the PCM achieved an accuracy of 97.20%, outperforming other state-of-the-art approaches reported in the literature if only trained with [8] (a result not shown in Table 3). A key aspect contributing to PCM success on Dataset #3 is its advanced preprocessing pipeline, which includes automated contrast enhancement and noise reduction tailored for chest X-ray images. Additionally, the model's architecture incorporates a multi-scale feature extraction mechanism, enabling it to capture both global patterns, such as lung opacity distribution, and local features, such as texture irregularities. These characteristics give PCM an edge over conventional and even some deep learning-based methods, which may struggle with variations in image quality and patient positioning.