Pneumonia Classification from X-ray Images with Inception-V3 and Convolutional Neural Network

Plant microbial fuel cells (PMFCs) are an emergent green-energy technology that continuously converts solar energy into electricity. Placing PMFCs on the roofs of urban buildings can help to create green urban environments even as they generate power. The power generation performance of PMFCs is affected by a range of environmental factors, so their power generation capacity is difficult to estimate. To develop an artificial intelligence model to forecast PMFC power generation accurately, relevant results obtained using shallow and deep learning techniques are compared for the first time. Once deep learning techniques had been identified as superior for this purpose, they were used with a bio-inspired optimization algorithm to dynamically setting the model hyperparameters. The developed model can also be applied to estimate the power generation capacity of PMFC devices in the future. The model was trained using data collected from sensors in a site experiment that was carried out using PMFCs embedded with Chinese pennisetumin (Pennisetum alopecuroides), narrowleaf cattail (Typha angustifolia), dwarf rotala (Rotala rotundifolia), and no plant as a control group. The original data of device parameters, environmental parameters, and the measured power generation of PMFCs in numerical form were applied to train shallow learning and time-series deep learning models. Meanwhile, the state-of-the-art sliding window technique was used to establish a numerical matrix, which was converted into a 2D image-like format to represent inputs for deep convolutional neural network (CNN) models. The accuracy in predicting the power generation capacity of PMFC devices showed that EfficientNet, an advanced type of CNN, was the best model among the shallow and deep learning techniques. These analytical results demonstrate the superior performance of deep CNNs in learning image features and their consequent suitability for constructing PMFC power generation forecasting models. To enhance the generalization performance of CNN, a

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“…Batch normalization is carried out extensively throughout the model and applied to activation inputs. Loss is computed using softmax 54 …”

Section: Methodsmentioning

confidence: 99%

“…Loss is computed using softmax. 54 Combining the InceptionV3 network architecture with ResNet residual blocks, Google proposed Inception-ResNet. The second version, 55 which offers better performance and faster training, is also used herein.…”

Section: Inceptionmentioning

confidence: 99%

Metaheuristics‐optimized deep learning to predict generation of sustainable energy from rooftop plant microbial fuel cells

Chou

Cheng

Liu

et al. 2022

Intl J of Energy Research

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“…Pneumonia is a leading cause of mortality, accounting for more than four million deaths annually [9]. In terms of timely detection methods, chest X-rays is a prominent diagnostic tool for pneumonia.…”

Section: Related Workmentioning

confidence: 99%

Automated Pneumonia Diagnosis using a 2D Deep Convolutional Neural Network with Chest X-Ray Images

Kassylkassova¹,

Omarov²,

Kazbekova³

et al. 2023

IJACSA

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Tiny air sacs in one or both lungs become inflamed as a result of the lung infection known as pneumonia. In order to provide the best possible treatment plan, pneumonia must be accurately and quickly diagnosed at initial stages. Nowadays, a chest X-ray is regarded as the most effective imaging technique for detecting pneumonia. However, performing chest X-ray analysis may be quite difficult and laborious. For this purpose, in this study we propose deep convolutional neural network (CNN) with 24 hidden layers to identify pneumonia using chest X-ray images. In order to get high accuracy of the proposed deep CNN we applied an image processing method as well as rescaling and data augmentation methods as shear_range, rotation, zooming, CLAHE, and vertical_flip. The proposed approach has been evaluated using different evaluation criteria and has demonstrated 97.2%, 97.1%, 97.43%, 96%, 98.8% performance in terms of accuracy, precision, recall, F-score, and AUC-ROC curve. Thus, the applied deep CNN obtain a high level of performance in pneumonia detection. In general, the provided approach is intended to aid radiologists in making an accurate pneumonia diagnosis. Additionally, our suggested models could be helpful in the early detection of other chest-related illnesses such as COVID-19.

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“…IV-A). As our first contribution is to provide a comparison between model complexity and its effect on robustness against attacks, we use five well-known pre-trained networks, such as LeNet5 [65], MobileNetV1 [66], VGG16 [67], ResNet50 [68], and InceptionV3 [69]. The first represents the model with the lowest computational cost compared to the others.…”

Section: Proposed Framework-based Secure Cnnmentioning

confidence: 99%

Secure Convolutional Neural Network-Based Internet-of-Healthcare Applications

et al. 2023

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Convolutional Neural Networks (CNNs) have gained popularity for Internet-of-Healthcare (IoH) applications such as medical diagnostics. However, new research shows that adversarial attacks with slight imperceptible changes can undermine deep neural network techniques in healthcare. This raises questions regarding the safety of deploying these IoH devices in clinical situations. In this paper, we review the techniques used in fighting against cyber-attacks. Then, we propose to study the robustness of some well-known CNN architectures' belonging to sequential, parallel, and residual families, such as LeNet5, MobileNetV1, VGG16, ResNet50, and InceptionV3 against Fast Gradient Sign Method (FGSM) and Projected Gradient Descent (PGD) attacks, in the context of classification of chest radiographs (Xrays) based on the IoH application. Finally, we propose to improve the security of these CNN structures by studying standard and adversarial training. The results show that, among these models, smaller models with lower computational complexity are more secure against hostile threats than larger models that are frequently used in IoH applications. In contrast, we reveal that when these networks are learned adversarially, they can outperform standard trained networks. The experimental results demonstrate that the model performance breakpoint is represented by γ = 0.3 with a maximum loss of accuracy tolerated at 2%.

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Pneumonia Classification from X-ray Images with Inception-V3 and Convolutional Neural Network

Cited by 62 publications

References 39 publications

Metaheuristics‐optimized deep learning to predict generation of sustainable energy from rooftop plant microbial fuel cells

Metaheuristics‐optimized deep learning to predict generation of sustainable energy from rooftop plant microbial fuel cells

Automated Pneumonia Diagnosis using a 2D Deep Convolutional Neural Network with Chest X-Ray Images

Secure Convolutional Neural Network-Based Internet-of-Healthcare Applications

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