Artificial intelligence classification model for macular degeneration images: a robust optimization framework for residual neural networks

Ho, Wen-Hsien; Huang, Tian-Hsiang; Yang, Po-Yuan; Chou, Jyh–Horng; Huang, Hong-Siang; Chi, Li-Chung; Chou, Fu-I; Tsai, Jinn-Tsong

doi:10.1186/s12859-021-04085-9

Cited by 8 publications

(5 citation statements)

References 13 publications

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“…The selection of an appropriate architecture is of paramount importance when addressing image time series tasks. Conventional convolutional neural networks (CNNs), such as the Visual Geometry Group (VGG) [30] and Residual Networks (ResNets) [31], are networks that exhibit certain limitations in processing image time-series data. While CNNs are effective in processing static images, they are more challenging to utilize in processing time-series data.…”

Section: Methodsmentioning

confidence: 99%

Predict Future Transient Fire Heat Release Rates Based on Fire Imagery and Deep Learning

Xu,

Dong,

Zou

2024

Fire

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The fire heat release rate (HRR) is a crucial parameter for describing the combustion process and its thermal effects. In recent years, some studies have employed fire scene images and deep learning algorithms to predict real-time fire HRR, which has led to the advancement of HRR prediction in terms of both lightweightness and real-time monitoring. Nevertheless, the development of an early-stage monitoring system for fires and the ability to predict future HRR based on current moment data represents a crucial foundation for evaluating the scale of indoor fires and enhancing the capacity to prevent and control such incidents. This paper proposes a deep learning model based on continuous fire scene images (containing both flame and smoke features) and their time-series information to predict the future transient fire HRR. The model (Att-BiLSTM) comprises three bi-directional long- and short-term memory (Bi-LSTM) layers and one attention layer. The model employs a bidirectional feature extraction approach, followed by the introduction of an attention mechanism to highlight the image features that have a critical impact on the prediction results. In this paper, a large-scale dataset is constructed by collecting 27,231 fire scene images with instantaneous HRR annotations from 40 different fire trials from the NIST database. The experimental results demonstrate that Att-BiLSTM is capable of effectively utilizing fire scene image features and temporal information to accurately predict future transient HRR, including those in high-brightness fire environments and complex fire source situations. The research presented in this paper offers novel insights and methodologies for fire monitoring and emergency response.

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Section: Methodsmentioning

confidence: 99%

Predict Future Transient Fire Heat Release Rates Based on Fire Imagery and Deep Learning

Xu,

Dong,

Zou

2024

Fire

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“…The dataset 42 ( https://www.kaggle.com/datasets/paultimothymooney/chest-xray-pneumonia ) is organized into the train, test, and validation directory, with a subdirectory for each image type ( P neumonia/ N ormal) within each directory. There are 5,856 CXR images in JPEG format, split into two categories (P/N).…”

Section: Methodsmentioning

confidence: 99%

Pneumonia detection with QCSA network on chest X-ray

Singh¹,

Kumar²,

Kumar

et al. 2023

Sci Rep

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Worldwide, pneumonia is the leading cause of infant mortality. Experienced radiologists use chest X-rays to diagnose pneumonia and other respiratory diseases. The diagnostic procedure's complexity causes radiologists to disagree with the decision. Early diagnosis is the only feasible strategy for mitigating the disease's impact on the patent. Computer-aided diagnostics improve the accuracy of diagnosis. Recent studies established that Quaternion neural networks classify and predict better than real-valued neural networks, especially when dealing with multi-dimensional or multi-channel input. The attention mechanism has been derived from the human brain's visual and cognitive ability in which it focuses on some portion of the image and ignores the rest portion of the image. The attention mechanism maximizes the usage of the image's relevant aspects, hence boosting classification accuracy. In the current work, we propose a QCSA network (Quaternion Channel-Spatial Attention Network) by combining the spatial and channel attention mechanism with Quaternion residual network to classify chest X-Ray images for Pneumonia detection. We used a Kaggle X-ray dataset. The suggested architecture achieved 94.53% accuracy and 0.89 AUC. We have also shown that performance improves by integrating the attention mechanism in QCNN. Our results indicate that our approach to detecting pneumonia is promising.

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“…Therefore, Chou et al [71] used experimental design method to find the best hyperparameter combination for a CNN. Ho et al [72] and Ho et al [73] also used experimental design method to explore hyperparameter combinations for deep residual network (ResNet) and CNN models. In summary, this paper explored the applicability of LSTM and GRU deep learning models for predicting DOC and used a Taguchi method to optimize hyperparameters.…”

Section: Related Workmentioning

confidence: 99%

Artificial Intelligence in Internet of Things System for Predicting Water Quality in Aquaculture Fishponds

Yang¹,

Liao²,

Chou³

2023

Computer Systems Science and Engineering

Self Cite

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Aquaculture has long been a critical economic sector in Taiwan. Since a key factor in aquaculture production efficiency is water quality, an effective means of monitoring the dissolved oxygen content (DOC) of aquaculture water is essential. This study developed an internet of things system for monitoring DOC by collecting essential data related to water quality. Artificial intelligence technology was used to construct a water quality prediction model for use in a complete system for managing water quality. Since aquaculture water quality depends on a continuous interaction among multiple factors, and the current state is correlated with the previous state, a model with time series is required. Therefore, this study used recurrent neural networks (RNNs) with sequential characteristics. Commonly used RNNs such as long short-term memory model and gated recurrent unit (GRU) model have a memory function that appropriately retains previous results for use in processing current results. To construct a suitable RNN model, this study used Taguchi method to optimize hyperparameters (including hidden layer neuron count, iteration count, batch size, learning rate, and dropout ratio). Additionally, optimization performance was also compared between 5-layer and 7-layer network architectures. The experimental results revealed that the 7-layer GRU was more suitable for the application considered in this study. The values obtained in tests of prediction performance were mean absolute percentage error of 3.7134%, root mean square error of 0.0638, and R-value of 0.9984. Therefore, the water quality management system developed in this study can quickly provide practitioners with highly accurate data, which is essential for a timely response to water quality issues. This study was performed in collaboration with the Taiwan Industrial Technology Research Institute and a local fishery company. Practical application of the system by the fishery company confirmed that the monitoring system is effective in improving the survival rate of farmed fish by providing data needed to maintain DOC higher than the standard value.

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Artificial intelligence classification model for macular degeneration images: a robust optimization framework for residual neural networks

Cited by 8 publications

References 13 publications

Predict Future Transient Fire Heat Release Rates Based on Fire Imagery and Deep Learning

Predict Future Transient Fire Heat Release Rates Based on Fire Imagery and Deep Learning

Pneumonia detection with QCSA network on chest X-ray

Artificial Intelligence in Internet of Things System for Predicting Water Quality in Aquaculture Fishponds

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