# PneumoNet: Artificial Intelligence Assistance for Pneumonia Detection on X-Rays
## 3 Pneumonet Framework
### Pneumonia Classification Module
Finally, to evaluate the comparison with radiologist performance of the PneumoNet's pneumonia classification module and address inter-reader variability, it was compared its performance to radiologist interpretations and other deep learning models from the literature for pneumonia detection in chest X-rays, specifically across Datasets #1 [8], #2 [9], and #3 [10]. The comparison draws from studies, including CheXNet [26], a VGG-16-based model [30], and a CNN with modified dropout [35], which provide insights into radiologist performance and AI-driven detection. Radiologists often face challenges in consistently identifying subtle signs of pneumonia, such as consolidations or ground-glass opacities, leading to moderate agreement among readers due to subjective interpretations, as noted in the literature. PCM demonstrated robust performance across all three datasets, reliably detecting pneumonia with fewer missed cases compared to typical radiologist interpretations. The CheXNet study [26], using a large chest X-ray dataset, showed that its model matched or surpassed radiologist performance in identifying pneumonia, particularly for complex cases similar to Dataset #1 [8]. PCM similarly excelled in detecting pneumonia patterns in this dataset, maintaining consistency across varied image qualities. The VGG-16 study [30] focused on Dataset #2 [9], highlighting strong detection of pneumonia features, though it faced challenges with subtle abnormalities compared to PCM's tailored architecture. For Dataset #3 [10], the CNN with modified dropout [35] showed reliable performance in paediatric cases, aligning with PCM's ability to handle such specialised data effectively. Radiologists bring strengths in integrating clinical context, such as patient symptoms or history, which PCM and other AI models do not incorporate. However, radiologist variability, stemming from difficulties in detecting subtle or paediatric findings, contrasts with the standardised predictions of PCM, CheXNet, and the CNN model. PCM's data augmentation and preprocessing enhance its robustness, complementing radiologist expertise. In summary, the PCM demonstrated strong robustness across the datasets, showing potential for real-world application in medical diagnostics. The tables also reflect the model's ability to generalise across different imaging modalities, underscoring its utility for multi-modal diagnostic tasks. The PneumoNet framework's pneumonia classification module leverages a modified AlexNet backbone, achieving exceptional performance (e.g., 98.70% accuracy, 99.70% AUC on Dataset #2 [9], surpassing VGG-16 at 96.40% [30] and MobileNet at 94.23% [20]) due to its tailored design for X-ray pneumonia detection. As shown, PCM retains AlexNet's convolutional base and adds a custom head: three \(3\times 3\) convolutional layers (16 filters each), batch normalisation, ReLU, and a fully connected layer with two neurones for binary classification. AlexNet's shallow architecture (8 layers, \(\sim\)60 million parameters) offers superior computational efficiency compared to DenseNet (\(\sim\)20 million parameters but intensive feature reuse), EfficientNet-B7 (\(\sim\)66 million parameters with complex scaling), or ConvNeXt (\(\sim\)90 million parameters with hierarchical convolutions). This efficiency enables rapid training (10 epochs, 128 mini-batch size, Adam optimiser with \(10^{-4}\) learning rate) on resource-constrained medical systems, critical for clinical deployment. AlexNet's straightforward convolutional design excels at capturing local X-ray features such as consolidations and opacities, outperforming denser architectures on smaller datasets such as #3 [10] (5863 images) by avoiding overfitting.