JPM | Free Full-Text | Concatenated Modified LeNet Approach for Classifying Pneumonia Images

By inergency On Mar 21, 2024

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4.2. Experimentation Details

In our experiments, we utilize Google Colab, a cloud-based platform offering Jupyter notebook functionality. This platform grants access to image sources through its connection to Google Drive. For accelerated training, we leverage T4 class GPUs offered by Google Colab. The deep learning models are built using Keras 2.6.0, a user-friendly library for constructing neural networks, which seamlessly integrates with TensorFlow 2.14.0, a powerful open-source framework for machine learning. This combined environment, accessible through Google Colab, allows for effortless library import and code execution within a collaborative setting. This integration simplifies model development while ensuring optimal utilization of the available GPU resources. The implementation of the concatenated model with the input images carried out using Python 3.10.11. The confusion matrix serves as a vital tool in evaluating the efficacy of a concatenated modified LeNet-5 model for image recognition, specifically in distinguishing between pneumonia and non-pneumonia images and the sample classification is shown in Figure 5. This matrix, typically presented as a 2 × 2 table for binary classification, encapsulates the counts of true positives (TP), true negatives (TN), false positives (FP), and false negatives (FN) based on the model’s predictions on a test dataset.

In the domain of pneumonia classification, we employ the concatenated modified LeNet-5 model. The evaluation of this model includes comprehensive performance metrics, shedding light on its efficacy in accurately classifying pneumonia cases. These metrics not only gauge the model’s accuracy but also provide insights into its precision, recall, and F1 score, allowing a nuanced understanding of its classification capabilities. The implications of these performance metrics extend beyond numerical values, offering crucial insights into the model’s strengths and areas for improvement. This analytical approach ensures a thorough assessment of the concatenated modified LeNet-5 model’s proficiency in pneumonia classification, providing valuable information for further refinement and optimization.

Sensitivity (True Positive Rate): This metric highlights the model’s ability to correctly identify images with pneumonia. It is calculated as TP divided by the sum of TP and FN. A higher sensitivity indicates a stronger capability to catch actual cases of pneumonia.
Specificity (True Negative Rate): Specificity gauges the model’s proficiency in correctly identifying non-pneumonia images. It is computed as TN divided by the sum of TN and FP. A higher specificity implies a reduced likelihood of misclassifying non-pneumonia instances.
Precision: Defined as the ratio of true positive predictions to the total instances predicted as positive (TP/(TP + FP)), precision measures the model’s ability to accurately identify pneumonia cases among all instances it predicts as positive. In the medical diagnosis context, precision becomes crucial as it reflects the proportion of predicted positive cases that are indeed true positives. High precision indicates that when the model predicts pneumonia, it is likely to be correct, minimizing the risk of false alarms in clinical settings.
F1-score: The F1-score combines precision and recall, striking a balance between the two metrics. It is the harmonic mean of precision and recall, providing a comprehensive measure of a model’s performance by considering both false positives and false negatives. The F1-score is particularly valuable in medical diagnosis, where achieving equilibrium between correctly identifying pneumonia cases (recall) and ensuring those predictions are accurate (precision) is paramount. A higher F1-score signifies a model that excels in both precision and recall, crucial for reliable and trustworthy pneumonia detection. Additionally, support indicates the number of actual occurrences of each class, providing context to the precision and recall values and aiding in the interpretation of the model’s performance across different class sizes.

In a medical diagnosis scenario, understanding the support for pneumonia and non-pneumonia classes helps to contextualize the model’s generalization to real-world prevalence rates, contributing to a more nuanced assessment of its effectiveness in clinical applications. The confusion matrix offered a detailed insight into how well the concatenated modified LeNet-5 architecture performs in classifying pneumonia cases, which is shown in Figure 6. Table 1 and Figure 7 illustrate the model’s efficacy in image recognition. It accurately distinguishes between 293 non-pneumonia cases and 824 pneumonia cases, showcasing high true negative and true positive values, respectively. However, it does show 22 false positives, indicating instances where non-pneumonia cases were mistakenly identified as pneumonia. Additionally, there are 33 false negatives, where the model incorrectly categorized pneumonia cases as non-pneumonia.

The confusion matrix is a powerful tool for assessing the performance of a classifier, offering insights that go beyond simple accuracy metrics and providing a more detailed picture of the model’s strengths and weaknesses. The concatenated LeNet5 model, as described, is a unique architecture that involves the combination of three identical LeNet-5 models. Each LeNet-5 model processes the input data independently through a series of convolutional and fully connected layers. The key features of this model are its use of three parallel LeNet-5 structures and the concatenation of their outputs. The use of three identical LeNet-5 models in parallel can be seen as a form of ensemble learning. Ensemble methods combine multiple models to improve overall performance, and in this case, the concatenation of outputs may capture diverse representations from the input data.

Feature Diversity: Each LeNet-5 model processes the input data independently, capturing different aspects and features. Concatenating these outputs likely results in a more comprehensive representation of the input data, contributing to improved accuracy.
Parameter Sharing: Since the LeNet-5 models are identical, they share the same set of parameters. This can help in reducing the overall model complexity while still benefiting from the parallel processing of multiple instances.
Effective Representation Learning: The architecture’s ability to achieve higher accuracy on both training and testing datasets suggests that it effectively learns and generalizes representations from the input data, outperforming other architectures like Modified LeNet, ResNet 50, and AlexNet.
Insight into Class Imbalances: In situations where there is a class imbalance (significant difference in the number of instances between classes), a confusion matrix helps identify how well the model performs for each class.
Model Comparison: When comparing multiple models, a confusion matrix facilitates a side-by-side evaluation of their performance, enabling stakeholders to make informed decisions about which model is better suited for a particular task.
Diagnostic Information: The confusion matrix is particularly useful in medical and diagnostic applications, providing information on the model’s ability to correctly identify positive (disease presence) and negative (disease absence) cases.

The concatenated LeNet-5 model employs a parallel processing approach, leveraging three identical LeNet-5 structures to capture diverse features from the input data. The concatenation of these outputs appears to enhance the model’s representation learning, leading to superior training and testing accuracies of 98% and 95%, respectively, compared to other specified architectures. Modified concatenated LeNet-5 architecture demonstrated significant improvements in image recognition tasks, particularly in distinguishing between pneumonia and non-pneumonia cases, even when dealing with large datasets which can be inferred from Table 2.

Figure 8 shows the implications of a multiclass ROC curve drawn based on a true positive (TP) versus false positive (FP) for a concatenated M=modified LeNet-5 model on a pneumonia dataset and its predictive power is depicted in Figure 9. The area under the ROC curve (AUC) of the multiclass ROC curve can provide an overall measure of how well the concatenated modified LeNet-5 model discriminated between pneumonia and non-pneumonia classes. Here, AUC of 0.99 is obtained which is higher AUC value. From this, it is inferred that the model has shown the better overall performance. The true positive rate (TPR), or sensitivity metric, indicated the ability of the model to correctly identify instances of pneumonia. A high sensitivity is crucial for a medical diagnosis task as it minimized the number of false negatives, ensuring that actual cases of pneumonia are not missed.

The false positive rate (FPR), or specificity metric, reflected the rate of misclassification of non-pneumonia instances as pneumonia. In a medical context, minimizing false positives is essential to avoid unnecessary treatments or interventions. The model’s performance is crucial for clinical decision support, particularly in accurately identifying pneumonia cases, as it directly impacts patient care. It is essential to emphasize that incorporating domain experts’ knowledge is key when interpreting the model’s results in medical applications. This collaboration will ensure that the model’s predictions align with clinical expectations, guiding appropriate patient care, medication decisions, and therapy details. The involvement of healthcare professionals is paramount to the successful integration of the model into the clinical decision-making process.

The design of the concatenated LeNet-5 model, which achieved higher training and testing accuracies compared to classifiers like modified LeNet, ResNet 50, and AlexNet, suggested several benefits that contribute to its superior performance, which are shown in Table 2 and Table 3 and Figure 10.

Ensemble Learning: The use of three identical LeNet-5 models in parallel introduced an ensemble learning strategy. Ensemble methods combine multiple models to improve overall performance by capturing diverse patterns and representations. In the concatenated LeNet-5 model, the parallel processing of three identical models enabled the extraction of complementary features from the input data. The subsequent concatenation of these diverse features contributes to a more robust and generalized model.
Comprehensive Feature Extraction: The concatenated LeNet-5 architecture allowed for a comprehensive extraction of features from the input data. Each LeNet-5 model processed the input independently, capturing different aspects and details. By concatenating these outputs, the model can aggregate a more extensive set of features, enhancing its ability to discriminate between classes and improving accuracy.
Parameter Sharing: The three LeNet-5 models in the concatenated LeNet-5 architecture are identical, meaning they shared the same set of parameters. Parameter sharing reduced the overall model complexity, making it more efficient and preventing overfitting on the training data. This shared parameterization also facilitates effective learning and generalization, contributing to the model’s high accuracy on both training and testing datasets.
Reduction of Overfitting: The ensemble nature of the concatenated LeNet-5 model helped mitigate overfitting. Overfitting occurred when a model learnt to perform well on the training data but failed to generalize to unseen data. The diversity introduced by parallel LeNet-5 models and their subsequent combination through concatenation aids in reducing overfitting, leading to better generalization and higher testing accuracy.
Parallel Processing and Efficiency: The parallel processing of three LeNet-5 models allows for efficient computation, enabling faster training times. This can be particularly advantageous when dealing with large datasets and complex architectures. The efficiency gained through parallel processing contributes to quicker convergence during training, resulting in a model that achieves higher accuracy in a shorter amount of time.
Effective Learning Representations: The Concatenated LeNet-5 model exceled in learning effective representations of the input data. The combination of parallel processing, ensemble learning, and concatenation of outputs results in a model that captured both low-level and high-level features, contributing to its ability to make accurate predictions.

The concatenated LeNet-5 model achieved higher training and testing accuracies compared to Modified LeNet, ResNet 50, and AlexNet through a combination of ensemble learning, comprehensive feature extraction, parameter sharing, reduction of overfitting, and efficient parallel processing. These design choices collectively contributed to a model that is not only powerful in learning from the training data but also effective in generalizing to unseen test data, leading to impressive accuracy rates.

The success of the concatenated LeNet-5 models in achieving higher precision, recall, F1-score, and support compared to other models signifies its proficiency in both minimizing false positives and false negatives and maximizing overall predictive performance. The concatenated LeNet-5 model accomplished this using three identical LeNet-5 models in parallel, constituting an ensemble learning approach. In the concatenated LeNet-5 model, the ensemble learning strategy aided in creating a more robust and reliable predictor. The three models processed the same input data independently and captured the diverse patterns and representations. The concatenation of the outputs allowed the integration of these features, providing a richer representation of the data. This comprehensive feature extraction is crucial for achieved higher precision, recall, F1 score, and support, as it enabled the model to discern subtle patterns and variations in the input. Parameter sharing promotes efficient learning and generalization, preventing overfitting of the training data. The shared parameters contribute to the model’s ability to discriminate between different classes by capturing common features across instances. This shared knowledge enhanced precision by reducing false positives and recall by minimizing false negatives.

The diversity introduced by the ensemble models, combined with concatenation, reduces the risk of overfitting, and improves the model’s capacity to generalize, positively impacting precision, recall, and the F1 score. The concatenated LeNet-5 model excels in learning effective representations of the input data. By combining the strengths of three LeNet-5 models, the architecture captures both low-level and high-level features, enabling more accurate predictions. This contributes to higher precision and recall, as the model is adept at identifying relevant patterns in the data.

The higher precision, recall, F1 score, and support indicate that the concatenated LeNet-5 model offers a more comprehensive and accurate evaluation of its predictive performance across multiple metrics which is shown in Figure 11 in comparison with other related benchmarked models. This is particularly valuable in real-world scenarios where different aspects of model performance are crucial. The higher precision and recall suggest that the concatenated LeNet-5 model is more confident in its predictions. This is crucial in applications where the consequences of false positives or false negatives are significant, such as in medical diagnoses or fraud detection. The model’s ability to achieve high precision, recall, F1 score, and support implied that it generalized well for unseen data. This is a key consideration in deploying machine learning models in real-world scenarios where robust performance on new, previously unseen instances is essential. In summary, the concatenated LeNet-5 model’s success in achieving higher precision, recall, F1 score, and support is attributed to its ensemble learning strategy, comprehensive feature extraction, parameter sharing, and efficient learning representations. The implications for model analysis include a holistic evaluation, robustness across various metrics, increased confidence in predictions, and strong generalization for unseen data [39].

The advantages of the concatenated deep learning classifiers framework include:

Improved performance in confirming infected cases
Flexibility in using different fine-tuned deep learning models
Ability to handle multi-label classification of X-ray images
Successful testing and evaluation on a public X-ray image dataset

This framework has the potential to significantly enhanced the accuracy of CAD systems for diagnosing pneumonia diseases.

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