Convolutional Encoder Decoder Research Articles

Publisher Correction: Twinenet: coupling features for synthesizing volume rendered images via convolutional encoder–decoders and multilayer perceptrons

Publisher Correction: Twinenet: coupling features for synthesizing volume rendered images via convolutional encoder–decoders and multilayer perceptrons

Crop pest identification using deep network based extracted features and MobileENet in smart agriculture

AbstractAgriculture has been considered an important source of food for humans throughout history. Plant pests cause significant damage to crops and reduce the productivity of global crop yields. Therefore, it is important to identify the plant pest at an earlier stage in order to minimize crop losses and use pesticides optimally. This paper develops the MobileENet deep learning architecture for accurate plant pest identification with less computational effort. The input images are pre‐processed, and the features are extracted using a deep convolutional encoder–decoder network (DCEDN). The proposed classification approach solves the problems of over‐fitting regularization, batch normalization, and dropout layers. Due to the minimum computing size and factorization process, the classification performance is increased. It extracts discriminatory feature information by eliminating redundant background information. The performance of the proposed approach is evaluated on the IP102 dataset, and the performance is compared with existing deep learning‐based approaches. The performance metrics, such as accuracy, precision, recall, and so forth, are considered to evaluate the performance of the proposed plant pest identification approach. The accuracy performance of the proposed approach is improved to 98.83% with less information loss.

A novel fusion of genetic grey wolf optimization and kernel extreme learning machines for precise diabetic eye disease classification.

In response to the growing number of diabetes cases worldwide, Our study addresses the escalating issue of diabetic eye disease (DED), a significant contributor to vision loss globally, through a pioneering approach. We propose a novel integration of a Genetic Grey Wolf Optimization (G-GWO) algorithm with a Fully Convolutional Encoder-Decoder Network (FCEDN), further enhanced by a Kernel Extreme Learning Machine (KELM) for refined image segmentation and disease classification. This innovative combination leverages the genetic algorithm and grey wolf optimization to boost the FCEDN's efficiency, enabling precise detection of DED stages and differentiation among disease types. Tested across diverse datasets, including IDRiD, DR-HAGIS, and ODIR, our model showcased superior performance, achieving classification accuracies between 98.5% to 98.8%, surpassing existing methods. This advancement sets a new standard in DED detection and offers significant potential for automating fundus image analysis, reducing reliance on manual examination, and improving patient care efficiency. Our findings are crucial to enhancing diagnostic accuracy and patient outcomes in DED management.

Enhancing Oil–Water Flow Prediction in Heterogeneous Porous Media Using Machine Learning

The rapid and accurate forecasting of two-phase flow in porous media is a critical challenge in oil field development, exerting a substantial impact on optimization and decision-making processes. Although the Convolutional Long Short-Term Memory (ConvLSTM) network effectively captures spatiotemporal dynamics, its generalization in predicting complex engineering problems remains limited. Similarly, although the Fourier Neural Operator (FNO) demonstrates adeptness at learning operators for solving partial differential equations (PDEs), it struggles with three-dimensional, long-term prediction. In response to these limitations, we introduce an innovative hybrid model, the Convolutional Long Short-Term Memory-Fourier Neural Operator (CL-FNO), specifically designed for the long-term prediction of three-dimensional two-phase flows. This model integrates a 3D convolutional encoder–decoder structure to extract and generate hierarchical spatial features of the flow fields. It incorporates physical constraints to enhance the model’s forecasts with robustness through the infusion of prior knowledge. Additionally, a temporal function, constructed using gated memory-forgetting mechanisms, augments the model’s capacity to analyze time series data. The efficacy and practicality of the CL-FNO model are validated using a synthetic three-dimensional case study and application to an actual reservoir model.

A novel SpaSA based hyper-parameter optimized FCEDN with adaptive CNN classification for skin cancer detection

Skin cancer is the most prevalent kind of cancer in people. It is estimated that more than 1 million people get skin cancer every year in the world. The effectiveness of the disease’s therapy is significantly impacted by early identification of this illness. Preprocessing is the initial detecting stage in enhancing the quality of skin images by removing undesired background noise and objects. This study aims is to compile preprocessing techniques for skin cancer imaging that are currently accessible. Researchers looking into automated skin cancer diagnosis might use this article as an excellent place to start. The fully convolutional encoder–decoder network and Sparrow search algorithm (FCEDN-SpaSA) are proposed in this study for the segmentation of dermoscopic images. The individual wolf method and the ensemble ghosting technique are integrated to generate a neighbour-based search strategy in SpaSA for stressing the correct balance between navigation and exploitation. The classification procedure is accomplished by using an adaptive CNN technique to discriminate between normal skin and malignant skin lesions suggestive of disease. Our method provides classification accuracies comparable to commonly used incremental learning techniques while using less energy, storage space, memory access, and training time (only network updates with new training samples, no network sharing). In a simulation, the segmentation performance of the proposed technique on the ISBI 2017, ISIC 2018, and PH2 datasets reached accuracies of 95.28%, 95.89%, 92.70%, and 98.78%, respectively, on the same dataset and assessed the classification performance. It is accurate 91.67% of the time. The efficiency of the suggested strategy is demonstrated through comparisons with cutting-edge methodologies.

Twinenet: coupling features for synthesizing volume rendered images via convolutional encoder–decoders and multilayer perceptrons

Twinenet: coupling features for synthesizing volume rendered images via convolutional encoder–decoders and multilayer perceptrons

CT-γ-Net: A Hybrid Model based on Convolutional Encoder-Decoder and Transformer Encoder for Brain Tumor Localization

Brain Tumor is a life-threatening disease, and its early diagnosis can save human life. Computer-Aided Brain Tumor Segmentation or Localization in Magnetic Resonance Imaging (MRI) images have emerged as pivotal approaches for expediting the disease diagnosis process. In the past few decades, various researchers combined the strengths of Convolutional Networks and Transformer to perform Brain Tumor Segmentation. However, these models require a large number of trainable weights parameters, and there is still scope for performance improvement in them. To bridge these research gaps, this paper proposes a novel hybrid model named “CT-γ-Net” for effective and efficient Brain Tumor Localization. The proposed CT-γ-Net model follows an Encoder-Decoder structure in which the Convolutional Encoder (CE) and Transformer Encoder (TE) are used for encoding, whereas the Convolutional Decoder (CD) is utilized for decoding the combined output of CE and TE to generate the segmentation masks. In CE and CD components of CT-γ-Net model conventional convolutional layers are replaced by Depth-Wise Separable convolutional layers, as these layers significantly reduce trainable weights parameters. The proposed model achieves 95.5% MeanIoU, 94.82% Dice Score, and 99.24% Pixel Accuracy on publicly available dataset named The Cancer Imaging Archive (TCIA). These experimental results demonstrate that the CT-γ-Net model outperformed other state-of-the-art research works, despite using roughly 28% fewer trainable weights parameters. Hence, the proposed model’s lightweight nature and its high performance, makes it a suitable candidate for deployment on mobile devices, facilitating the precise localization of brain tumor regions in MRI images.

Deep convolutional encoder–decoder networks based on ensemble learning for semantic segmentation of high-resolution aerial imagery

Deep convolutional encoder–decoder networks based on ensemble learning for semantic segmentation of high-resolution aerial imagery

Predicting Time-to-Healing from a Digital Wound Image: A Hybrid Neural Network and Decision Tree Approach Improves Performance

Despite the societal burden of chronic wounds and despite advances in image processing, automated image-based prediction of wound prognosis is not yet in routine clinical practice. While specific tissue types are known to be positive or negative prognostic indicators, image-based wound healing prediction systems that have been demonstrated to date do not (1) use information about the proportions of tissue types within the wound and (2) predict time-to-healing (most predict categorical clinical labels). In this work, we analyzed a unique dataset of time-series images of healing wounds from a controlled study in dogs, as well as human wound images that are annotated for the tissue type composition. In the context of a hybrid-learning approach (neural network segmentation and decision tree regression) for the image-based prediction of time-to-healing, we tested whether explicitly incorporating tissue type-derived features into the model would improve the accuracy for time-to-healing prediction versus not including such features. We tested four deep convolutional encoder–decoder neural network models for wound image segmentation and identified, in the context of both original wound images and an augmented wound image-set, that a SegNet-type network trained on an augmented image set has best segmentation performance. Furthermore, using three different regression algorithms, we evaluated models for predicting wound time-to-healing using features extracted from the four best-performing segmentation models. We found that XGBoost regression using features that are (i) extracted from a SegNet-type network and (ii) reduced using principal components analysis performed the best for time-to-healing prediction. We demonstrated that a neural network model can classify the regions of a wound image as one of four tissue types, and demonstrated that adding features derived from the superpixel classifier improves the performance for healing-time prediction.

An approach to ensure joint illumination & communication performance of a forward error corrected indoor visible light communication (VLC) system in presence of ambient light interference

Abstract In recent time of looming radio frequency (RF) spectrum crisis, visible light communication using lighting infrastructure emerged as a potential alternative at an indoor environment. This paper addresses the setback associated with ambient light interference in an indoor Visible Light Communication (VLC) system to ensure joint communication and illumination performance inside an office room. A novel VLC architecture with suitable white light emitting diode (WLED) luminaire arrangement is presented to minimize the dispersion of signal to interference plus noise ratio (SINR) across the room. Luminaires are categorized in two groups viz. data transmitting illuminants and illuminants for lighting purpose. The first group is dedicated to transmit data as well as serves the purpose of illumination. The other set creates only ambient illumination to achieve quality lighting attributes. The proposed forward error corrected receiver configuration discards the ambient light noise originated by the illuminants that serve the ambient illumination. Tail biting convolutional encoder and viterbi decoder are used at the encoding section of the transmitter and decoding section of the receiver respectively to improve bit error rate. Results obtained through MATLAB simulation shows better average bit error rate (BER) in the order of 10−8 measured at uniformly distributed 25 grid points over the working plane. At the same time achieved average horizontal illuminance with good uniformity comply with ISO recommendation.

A Deep Convolutional Encoder–Decoder–Restorer Architecture for Image Deblurring

The accuracy of many computer vision tasks is reduced by blurred images, so deblur is important. More details of the image can be captured by a common multi-stage network, but the computational complexity of this method is higher compared with a single-stage network. However, a single-stage network cannot capture multi-scale information well. To tackle the problem, a novel convolutional encoder–decoder–restorer architecture is proposed. In this architecture, a multi-scale input structure is used in the encoder. Improved supervised attention module is inserted into the encoder for enhanced feature acquisition. In decoder, information supplement block is proposed to fuse multi-scale features. Finally, the fused features are used for image recovery in the restorer. In order to optimise the model in multiple domains, the loss function is calculated separately in the spatial and frequency domains. Our method is compared with existing methods on the GOPRO dataset. In addition, to verify the applications of our proposed method, we conduct experiments on the Real image dataset, the VOC2007 dataset and the LFW dataset. Experimental results show that our proposed method outperforms state-of-the-art deblurring methods and improves the accuracy of different vision tasks.

ITrans: generative image inpainting with transformers

Despite significant improvements, convolutional neural network (CNN) based methods are struggling with handling long-range global image dependencies due to their limited receptive fields, leading to an unsatisfactory inpainting performance under complicated scenarios. To address this issue, we propose the Inpainting Transformer (ITrans) network, which combines the power of both self-attention and convolution operations. The ITrans network augments convolutional encoder–decoder structure with two novel designs, i.e. , the global and local transformers. The global transformer aggregates high-level image context from the encoder in a global perspective, and propagates the encoded global representation to the decoder in a multi-scale manner. Meanwhile, the local transformer is intended to extract low-level image details inside the local neighborhood at a reduced computational overhead. By incorporating the above two transformers, ITrans is capable of both global relationship modeling and local details encoding, which is essential for hallucinating perceptually realistic images. Extensive experiments demonstrate that the proposed ITrans network outperforms favorably against state-of-the-art inpainting methods both quantitatively and qualitatively.

DSLSTM: a deep convolutional encoder–decoder architecture for abnormality detection in video surveillance

DSLSTM: a deep convolutional encoder–decoder architecture for abnormality detection in video surveillance

Human-in-the-loop latent space learning for biblio-record-based literature management

Every researcher must conduct a literature review, and the document management needs of researchers working on various research topics vary. However, there are two major challenges. First, traditional methods such as the tree hierarchy of document folders and tag-based management are no longer effective with the enormous volume of publications. Second, although their bibliographic information is available to everyone, many papers can only be accessed through paid services. This study attempts to develop an interactive tool for personal literature management based solely on their bibliographic records. To make such a tool possible, we developed a principled “human-in-the-loop latent space learning” method that estimates the management criteria of each researcher based on his or her feedback to calculate the positions of documents in a two-dimensional space on the screen. As a set of bibliographic records forms a graph, our model is naturally designed as a graph-based encoder–decoder model that connects the graph and the space. In addition, we also devised an active learning framework using uncertainty sampling for it. The challenge here is to define the uncertainty in a problem setting. Experiments with ten researchers from the humanities, science, and engineering domains show that the proposed framework provides superior results to a typical graph convolutional encoder–decoder model. In addition, we found that our active learning framework was effective in selecting good samples.

Turning brain MRI into diagnostic PET: 15O-water PET CBF synthesis from multi-contrast MRI via attention-based encoder–decoder networks

Accurate quantification of cerebral blood flow (CBF) is essential for the diagnosis and assessment of a wide range of neurological diseases. Positron emission tomography (PET) with radiolabeled water (15O-water) is the gold-standard for the measurement of CBF in humans, however, it is not widely available due to its prohibitive costs and the use of short-lived radiopharmaceutical tracers that require onsite cyclotron production. Magnetic resonance imaging (MRI), in contrast, is more accessible and does not involve ionizing radiation. This study presents a convolutional encoder–decoder network with attention mechanisms to predict the gold-standard 15O-water PET CBF from multi-contrast MRI scans, thus eliminating the need for radioactive tracers. The model was trained and validated using 5-fold cross-validation in a group of 126 subjects consisting of healthy controls and cerebrovascular disease patients, all of whom underwent simultaneous 15O-water PET/MRI. The results demonstrate that the model can successfully synthesize high-quality PET CBF measurements (with an average SSIM of 0.924 and PSNR of 38.8 dB) and is more accurate compared to concurrent and previous PET synthesis methods. We also demonstrate the clinical significance of the proposed algorithm by evaluating the agreement for identifying the vascular territories with impaired CBF. Such methods may enable more widespread and accurate CBF evaluation in larger cohorts who cannot undergo PET imaging due to radiation concerns, lack of access, or logistic challenges.

Convolutional encoder–decoder network using transfer learning for topology optimization

Convolutional encoder–decoder network using transfer learning for topology optimization

A Physics-Informed Spatial-Temporal Neural Network for Reservoir Simulation and Uncertainty Quantification

Summary Surrogate models play a vital role in reducing computational complexity and time burden for reservoir simulations. However, traditional surrogate models suffer from limitations in autonomous temporal information learning and restrictions in generalization potential, which is due to a lack of integration with physical knowledge. In response to these challenges, a physics-informed spatial-temporal neural network (PI-STNN) is proposed in this work, which incorporates flow theory into the loss function and uniquely integrates a deep convolutional encoder-decoder (DCED) with a convolutional long short-term memory (ConvLSTM) network. To demonstrate the robustness and generalization capabilities of the PI-STNN model, its performance was compared against both a purely data-driven model with the same neural network architecture and the renowned Fourier neural operator (FNO) in a comprehensive analysis. Besides, by adopting a transfer learning strategy, the trained PI-STNN model was adapted to the fractured flow fields to investigate the impact of natural fractures on its prediction accuracy. The results indicate that the PI-STNN not only excels in comparison with the purely data-driven model but also demonstrates a competitive edge over the FNO in reservoir simulation. Especially in strongly heterogeneous flow fields with fractures, the PI-STNN can still maintain high prediction accuracy. Building on this prediction accuracy, the PI-STNN model further offers a distinct advantage in efficiently performing uncertainty quantification, enabling rapid and comprehensive analysis of investment decisions in oil and gas development.

Deep understanding of radiology reports: leveraging dynamic convolution in chest X-ray images

PurposeThe purpose of this study is to investigate and demonstrate the advancements achieved in the field of chest X-ray image captioning through the utilization of dynamic convolutional encoder–decoder networks (DyCNN). Typical convolutional neural networks (CNNs) are unable to capture both local and global contextual information effectively and apply a uniform operation to all pixels in an image. To address this, we propose an innovative approach that integrates a dynamic convolution operation at the encoder stage, improving image encoding quality and disease detection. In addition, a decoder based on the gated recurrent unit (GRU) is used for language modeling, and an attention network is incorporated to enhance consistency. This novel combination allows for improved feature extraction, mimicking the expertise of radiologists by selectively focusing on important areas and producing coherent captions with valuable clinical information.Design/methodology/approachIn this study, we have presented a new report generation approach that utilizes dynamic convolution applied Resnet-101 (DyCNN) as an encoder (Verelst and Tuytelaars, 2019) and GRU as a decoder (Dey and Salemt, 2017; Pan et al., 2020), along with an attention network (see Figure 1). This integration innovatively extends the capabilities of image encoding and sequential caption generation, representing a shift from conventional CNN architectures. With its ability to dynamically adapt receptive fields, the DyCNN excels at capturing features of varying scales within the CXR images. This dynamic adaptability significantly enhances the granularity of feature extraction, enabling precise representation of localized abnormalities and structural intricacies. By incorporating this flexibility into the encoding process, our model can distil meaningful and contextually rich features from the radiographic data. While the attention mechanism enables the model to selectively focus on different regions of the image during caption generation. The attention mechanism enhances the report generation process by allowing the model to assign different importance weights to different regions of the image, mimicking human perception. In parallel, the GRU-based decoder adds a critical dimension to the process by ensuring a smooth, sequential generation of captions.FindingsThe findings of this study highlight the significant advancements achieved in chest X-ray image captioning through the utilization of dynamic convolutional encoder–decoder networks (DyCNN). Experiments conducted using the IU-Chest X-ray datasets showed that the proposed model outperformed other state-of-the-art approaches. The model achieved notable scores, including a BLEU_1 score of 0.591, a BLEU_2 score of 0.347, a BLEU_3 score of 0.277 and a BLEU_4 score of 0.155. These results highlight the efficiency and efficacy of the model in producing precise radiology reports, enhancing image interpretation and clinical decision-making.Originality/valueThis work is the first of its kind, which employs DyCNN as an encoder to extract features from CXR images. In addition, GRU as the decoder for language modeling was utilized and the attention mechanisms into the model architecture were incorporated.

Underwater Acoustic Signal Noise Reduction Based on a Fully Convolutional Encoder-Decoder Neural Network

Underwater Acoustic Signal Noise Reduction Based on a Fully Convolutional Encoder-Decoder Neural Network

A deep learning based surrogate model for reservoir dynamic performance prediction

Predicting the reservoir dynamic performance is an important basis for exploiting the remaining oil and improving the recovery. Traditional prediction techniques primarily include numerical simulation and reservoir engineering methods, which show technical drawbacks such as low computational efficiency and too simplified assumptions. To tackle this challenge, we propose a surrogate model for reservoir dynamic performance prediction based on a Variety of View deep convolutional encoder-decoder (VoV-DCED) network. The influence of production time is accounted for using dimension expansion. Our model directly predicts the remaining oil saturation distribution with known reservoir permeability maps and varying well schedules. We examine the performance of our model in 2D and 3D waterflooding reservoirs, which shows that the prediction results are in good consistence with the reservoir numerical simulation. The model can be extended to account for the impacts of other factors, such as initial oil saturation distribution, well patterns, and non-frequent heterogeneities. Although deep learning takes a longer time for dataset preparation in comparison to the traditional simulation, the prediction efficiency of our trained model is almost two orders of magnitude higher than that of numerical simulation. Therefore, this surrogate model can act as an efficient tool for automatic history matching and production optimization—which require multiple simulation runs.