Neural Image Research Articles

Classifying histopathological growth patterns for resected colorectal liver metastasis with a deep learning analysis.

Histopathological growth patterns are one of the strongest prognostic factors in patients with resected colorectal liver metastases. Development of an efficient, objective and ideally automated histopathological growth pattern scoring method can substantially help the implementation of histopathological growth pattern assessment in daily practice and research. This study aimed to develop and validate a deep-learning algorithm, namely neural image compression, to distinguish desmoplastic from non-desmoplastic histopathological growth patterns of colorectal liver metastases based on digital haematoxylin and eosin-stained slides. The algorithm was developed using digitalized whole-slide images obtained in a single-centre (Erasmus MC Cancer Institute, the Netherlands) cohort of patients who underwent first curative intent resection for colorectal liver metastases between January 2000 and February 2019. External validation was performed on whole-slide images of patients resected between October 2004 and December 2017 in another institution (Radboud University Medical Center, the Netherlands). The outcomes of interest were the automated classification of dichotomous hepatic growth patterns, distinguishing between desmoplastic hepatic growth pattern and non-desmoplatic growth pattern by a deep-learning model; secondary outcome was the correlation of these classifications with overall survival in the histopathology manual-assessed histopathological growth pattern and those assessed using neural image compression. Nine hundred and thirty-two patients, corresponding to 3.641 whole-slide images, were reviewed to develop the algorithm and 870 whole-slide images were used for external validation. Median follow-up for the development and the validation cohorts was 43 and 29 months respectively. The neural image compression approach achieved significant discriminatory power to classify 100% desmoplastic histopathological growth pattern with an area under the curve of 0.93 in the development cohort and 0.95 upon external validation. Both the histopathology manual-scored histopathological growth pattern and neural image compression-classified histopathological growth pattern achieved a similar multivariable hazard ratio for desmoplastic versus non-desmoplastic growth pattern in the development cohort (histopathology manual score: 0.63 versus neural image compression: 0.64) and in the validation cohort (histopathology manual score: 0.40 versus neural image compression: 0.48). The neural image compression approach is suitable for pathology-based classification tasks of colorectal liver metastases.

Temporomandibular joint CBCT image segmentation via multi-view ensemble learning network.

Accurate segmentation of the temporomandibular joint (TMJ) from cone beam CT (CBCT) images holds significant clinical value for diagnosing temporomandibular joint osteoarthrosis (TMJOA) and related conditions. Convolutional neural network-based medical image segmentation methods have achieved state-of-the-art performance in various segmentation tasks. However, 3D medical images segmentation requires substantial global context and rich spatial semantic information, demanding much more GPU memory and computational resources. To address these challenges in 3D medical image segmentation, we propose a novel network- the MVEL-Net (Multi-view Ensemble Learning Network) for TMJ CBCT image segmentation. By resampling images along three dimensions, we generate multiple weak learners with different spatial semantic information. A subsequent strong learning network effectively integrates the outputs from these weak learners to achieve more accurate segmentation results. We evaluated our network model using a clinical dataset comprising 88 subjects with TMJ CBCT images. The average Dice similarity coefficient (DSC) was 0.9817 ± 0.0049, the average surface distance was 0.0540 ± 0.0179mm, and the 95% Hausdorff distance was 0.1743 ± 0.0550mm. Our proposed MVEL-Net demonstrates excellent segmentation performance on TMJ from CBCT images, while using fewer GPU memory resources compared to other 3D networks. The effectiveness of this method in capturing spatial context could be leveraged for tasks like organ segmentation from volumetric scans. This may facilitate wider adoption of AI-based solutions for automated analysis of 3D medical images.

Non-Negative Matrix Factorization with Averaged Kurtosis and Manifold Constraints for Blind Hyperspectral Unmixing

The Nonnegative Matrix Factorization (NMF) algorithm and its variants have gained widespread popularity across various domains, including neural networks, text clustering, image processing, and signal analysis. In the context of hyperspectral unmixing (HU), an important task involving the accurate extraction of endmembers from mixed spectra, researchers have been actively exploring different regularization techniques within the traditional NMF framework. These techniques aim to improve the precision and reliability of the endmember extraction process in HU. In this study, we propose a novel HU algorithm called KMBNMF, which introduces an average kurtosis regularization term based on endmember spectra to enhance endmember extraction, additionally, it integrates a manifold regularization term into the average kurtosis-constrained NMF by constructing a symmetric weight matrix. This combination of these two regularization techniques not only optimizes the extraction process of independent endmembers but also improves the part-based representation capability of hyperspectral data. Experimental results obtained from simulated and real-world hyperspectral datasets demonstrate the competitive performance of the proposed KMBNMF algorithm when compared to state-of-the-art algorithms.

Defect Detection of Polyethylene Gas Pipeline Based on Convolutional Neural Networks and Image Processing

Abstract In this paper, a method based on image recognition was proposed to detect the defects of polyethylene (PE) gas pipeline, especially the deformation due to the indentation. First, the pipeline -detection VGG (PD-VGG) model was established based on the convolutional neural network (CNN), and appropriate model parameters were optimized through model training. The defect recognition rate of the improved model can reach 94.76%. Following, the weighted average graying algorithm was used to separate the defects characterized by deformation. Then, an improved gamma correction algorithm was applied to achieve image enhancement, and the interference of impurities adhered on intersurface of pipeline was also removed by using multilayer filters. The edge detection of the defect image was completed by using the Canny operator, and following the screening between the target contour and the interference contour by using top-contour. Finally, the algorithm for minimum outer rectangle algorithm was used to fit the defect contour, and the eigenvalues of deformation defects were extracted. The results indicate that the above defect detection method can better extract the deformation contour of the dented pipeline. The high agreement with the experimental results provides a basis for the research of effectively recognizing whether the pipeline has undergone ductile failure only through profile detection of defects.

Balancing the encoder and decoder complexity in image compression for classification

This paper presents a study on the computational complexity of coding for machines, with a focus on image coding for classification. We first conduct a comprehensive set of experiments to analyze the size of the encoder (which encodes images to bitstreams), the size of the decoder (which decodes bitstreams and predicts class labels), and their impact on the rate–accuracy trade-off in compression for classification. Through empirical investigation, we demonstrate a complementary relationship between the encoder size and the decoder size, i.e., it is better to employ a large encoder with a small decoder and vice versa. Motivated by this relationship, we introduce a feature compression-based method for efficient image compression for classification. By compressing features at various layers of a neural network-based image classification model, our method achieves adjustable rate, accuracy, and encoder (or decoder) size using a single model. Experimental results on ImageNet classification show that our method achieves competitive results with existing methods while being much more flexible. The code will be made publicly available.

Stable successive Neural Image Compression via coherent demodulation-based transformation

Neural Image Compression (NIC) has made significant strides in recent years. However, the existing NIC methods demonstrate instability issues during iterative re-compression cycles, which can degrade image quality with each cycle. This paper introduces a novel framework aimed at enhancing the stability of NIC methods. We first conducted a theoretical analysis and identified that the instability in current NIC methods stems from a lack of idempotency in transformations. Drawing from the domain of signal processing, we then examined the principles of idempotency in coherent demodulation techniques. This examination led to the identification of three foundational principles that inform the design of stable transformations: the cosine function, parameter sharing, and low-pass filtering. Leveraging these insights, we propose the innovative Coherent Demodulation-based Transformation (CDT), which is designed to address the stability challenges in NIC by incorporating these principles into its architecture. The experimental results suggest that CDT not only significantly improve the re-compression stability but also preserves the codec’s rate–distortion performance. Furthermore, it can be broadly applied in current NIC structures. The effectiveness of the module endorses the viability of designing transformation networks based on Coherent Demodulation principles, playing a crucial role in enhancing stability of NIC. The code will be available at https://github.com/baoyu2020/Stable_SuccessiveNIC.

Improved transfer learning two-branch convolutional neural network image dehazing

In view of the existing image dehazing algorithms, there are problems of incomplete dehazing and image color distortion. A dehazing network combining Transfer learning sub-net and Residual attention sub-net is proposed. First, the pretrained model of the transfer learning subnet is adopted to enhance the feature attributes of the samples. Second, the struct ure of the dual-branch network is constructed, and the residual attention sub-network is used to assist the transfer learning subnetwork to train the parameters of the network model. Finally, the method of tail ensemble learning is used to fuse the features of the dual network to obtain the model parameters of the dehazed image, so as to complete the image restoration task. The experimental results show that the algorithm proposed in the paper improves the PSNR index by 1.87 dB and 4.22 dB on the RESIDE data set and the O-HAZE data set respectively, and the SSIM index on the O-HAZE data set by 6.7%.

Image Restoration of Electrical Well Logging Based on Fourier Convolution

Imaging logging is an important technical means in logging evaluation of complex reservoirs. Through imaging logging, a two-dimensional image of the resistivity distribution around the well can be obtained, which can be used to evaluate the development of well wall fractures and caves and the formation sedimentary structure. However, due to the characteristics of resistivity imaging logging instruments, blank strips will appear on the resistivity logging image, which increases the difficulty of computer processing of electrical imaging data. The current image repair method and the existing neural network image repair method are not effective enough when the part to be filled is large. Therefore, there is an urgent need for an intelligent repair method based on deep learning. Based on a fast Fourier convolution-based imaging logging image blank strip filling network, this paper constructs the electrical imaging logging images of the southwest oil and gas field as a data set, and trains a new deep learning algorithm for intelligent filling of blank strips in imaging logging images based on fast Fourier convolution. The time of various algorithms is compared. The results show that the algorithm has a better repair effect in imaging logging images with large strip widths, and the repair efficiency is significantly improved. This method can realize the rapid, accurate and intelligent repair of blank strips in imaging logging, achieve the rapid generation of full-borehole images, and solve the difficulties in obtaining full-borehole images.

Single ambipolar OECT-based inverter with volatility and nonvolatility on demand.

Organic electrochemical transistor (OECT)-based inverter introduces new prospects for energy-efficient brain-inspired artificial intelligence devices. Here, we report single-component OECT-based inverters by incorporating ambipolar p(gDPP-V). Notably, p(gDPP-V) shows state-of-the-art ambipolar OECT performances in both conventional (p/n-type mode transconductance of 29/25 S cm-1) and vertical (transconductance of 297.2/292.4 μS μm-2 under p/n operation) device architectures. Especially, the resulting highly stable vertical OECT-based inverter shows a high voltage gain of 105 V V-1 under a low driving voltage of 0.8 V. The inverter exhibits undiscovered voltage-regulated dual mode: volatile receptor and nonvolatile synapse. Moreover, applications of physiology signal recording and demonstrations of NAND/NOR logic circuits are investigated within the volatile feature, while neuromorphic simulations with a convolutional neural network and image memorizing capabilities are explored under the nonvolatile behavior. The ambipolar OECT-based inverter, capable of both volatile and nonvolatile operations, provides possibilities for the applications of reconfigurable complementary logic circuits in novel neuromorphic computing paradigms.

High efficiency deep image compression via channel-wise scale adaptive latent representation learning

Recent learning based neural image compression methods have achieved impressive rate–distortion (RD) performance via the sophisticated context entropy model, which performs well in capturing the spatial correlations of latent features. However, due to the dependency on the adjacent or distant decoded features, existing methods require an inefficient serial processing structure, which significantly limits its practicability. Instead of pursuing computationally expensive entropy estimation, we propose to reduce the spatial redundancy via the channel-wise scale adaptive latent representation learning, whose entropy coding is spatially context-free and parallelizable. Specifically, the proposed encoder adaptively determines the scale of the latent features via a learnable binary mask, which is optimized with the RD cost. In this way, lower-scale latent representation will be allocated to the channels with higher spatial redundancy, which consumes fewer bits and vice versa. The downscaled latent features could be well recovered with a lightweight inter-channel upconversion module in the decoder. To compensate for the entropy estimation performance degradation, we further develop an inter-scale hyperprior entropy model, which supports the high efficiency parallel encoding/decoding within each scale of the latent features. Extensive experiments are conducted to illustrate the efficacy of the proposed method. Our method achieves bitrate savings of 18.23%, 19.36%, and 27.04% over HEVC Intra, along with decoding speeds that are 46 times, 48 times, and 51 times faster than the baseline method on the Kodak, Tecnick, and CLIC datasets, respectively.

Environmentally Sustainable Detection of Arsenic using Convolutional Neural Networks and Imidazole-Based Organic Probes: Application in Food Samples and Arsenic Album.

Arsenic contamination poses a significant health risk, particularly when it infiltrates water supplies. While current detection methods offer precise analysis, they often involve complex instrumentation not suitable for field use. This study presents a novel approach by developing two probes, A1 and A2, based on 4-diethylaminosalicyladehyde, 2-hydroxy-1-naphthaldehyde, and 1,2-diaminoanthraquinone. These probes are highly sensitive and selective for detecting arsenite (As(III)) and arsenate (As(V)) in water, food samples, and homeopathic medicine with limits of detection in the nanomolar range. To elaborate our contribution to on-site arsenic detection, we introduce a convolutional neural network-based image recognition system. This system interprets images of the probes' colorimetric response, effectively categorizing different ranges of arsenic concentrations in parts per million (ppm). Our approach offers a real-time, cost-effective, and user-friendly solution for arsenic detection, extending its applicability from scientific laboratories to in-field conditions and even household monitoring. The findings fill critical research gaps in real-time detection methods, potentially revolutionizing the way we monitor environmental contaminants like arsenic.

Deep Neural Remote Sensing and Sentinel-2 Satellite Image Processing of Kirkuk City, Iraq for Sustainable Prospective

Deep Neural Remote Sensing and Sentinel-2 Satellite Image Processing of Kirkuk City, Iraq for Sustainable Prospective

Semantic Segmentation of Urban Remote Sensing Images Based on Deep Learning

In the realm of urban planning and environmental evaluation, the delineation and categorization of land types are pivotal. This study introduces a convolutional neural network-based image semantic segmentation approach to delineate parcel data in remote sensing imagery. The initial phase involved a comparative analysis of various CNN architectures. ResNet and VGG serve as the foundational networks for training, followed by a comparative assessment of the experimental outcomes. Subsequently, the VGG+U-Net model, which demonstrated superior efficacy, was chosen as the primary network. Enhancements to this model were made by integrating attention mechanisms. Specifically, three distinct attention mechanisms—spatial, SE, and channel—were incorporated into the VGG+U-Net framework, and various loss functions were evaluated and selected. The impact of these attention mechanisms, in conjunction with different loss functions, was scrutinized. This study proposes a novel network model, designated VGG+U-Net+Channel, that leverages the VGG architecture as the backbone network in conjunction with the U-Net structure and augments it with the channel attention mechanism to refine the model’s performance. This refinement resulted in a 1.14% enhancement in the network’s overall precision and marked improvements in MPA and MioU. A comparative analysis of the detection capabilities between the enhanced and original models was conducted, including a pixel count for each category to ascertain the extent of various semantic information. The experimental validation confirms the viability and efficacy of the proposed methodology.

Diagnostics of Ship Engines Based on Wavelet Neural Network and Image Scanning Using Programmable Logic Circuit

Diagnostics of Ship Engines Based on Wavelet Neural Network and Image Scanning Using Programmable Logic Circuit

Analysis of Convolutional Neural Network-based Image Classifications: A Multi-Featured Application for Rice Leaf Disease Prediction and Recommendations for Farmers

This study presents a novel method for improving rice disease classification using 8 different convolutional neural network (CNN) algorithms, which will further the field of precision agriculture. A thorough investigation of deep learning methods is carried out using the UCI dataset in order to create a reliable and effective model that can correctly identify a range of rice diseases. The suggested transfer learning models performs better at identifying subtle features and complex patterns in the dataset, which results in extremely accurate disease classification. Moreover, the study goes beyond the creation of models by incorporating an intuitive Tkinter-based application that offers farmers a feature-rich interface. With the help of this cutting-edge application, farmers will be able to make timely and well-informed decisions by enabling real-time disease prediction and providing personalized recommendations. Together with the user-friendly Tkinter interface, the smooth integration of cutting-edge CNN transfer Learning algorithms-based technology that include ResNet-50, InceptionV3, VGG16, MobileNetv2 with the UCI dataset represents a major advancement toward modernizing agricultural practices and guaranteeing sustainable crop management. Remarkable outcomes include 75% accuracy for ResNet-50, 90% accuracy for DenseNet121, 84% accuracy for VGG16, 95.83% accuracy for MobileNetV2, 91.61% accuracy for DenseNet169, and 86% accuracy for InceptionV3. These results give a concise summary of the models' capabilities, assisting researchers in choosing appropriate strategies for precise and successful rice crop disease identification. A severe overfitting has been seen on VGG19 with 70% accuracy and Nasnet with 80.02% accuracy. On Renset101 only an accuracy of 54% could be achieved along with only 33% on efficientNetB0. MobileNetV2 trained model was successfully deployed on a tkinter GUI application to make predictions using image or real time video capturing.

Aggregated Pattern Classification Method for improving neural disorder stage detection.

Neurological disorders pose a significant health challenge, and their early detection is critical for effective treatment planning and prognosis. Traditional classification of neural disorders based on causes, symptoms, developmental stage, severity, and nervous system effects has limitations. Leveraging artificial intelligence (AI) and machine learning (ML) for pattern recognition provides a potent solution to address these challenges. Therefore, this study focuses on proposing an innovative approach-the Aggregated Pattern Classification Method (APCM)-for precise identification of neural disorder stages. The APCM was introduced to address prevalent issues in neural disorder detection, such as overfitting, robustness, and interoperability. This method utilizes aggregative patterns and classification learning functions to mitigate these challenges and enhance overall recognition accuracy, even in imbalanced data. The analysis involves neural images using observations from healthy individuals as a reference. Action response patterns from diverse inputs are mapped to identify similar features, establishing the disorder ratio. The stages are correlated based on available responses and associated neural data, with a preference for classification learning. This classification necessitates image and labeled data to prevent additional flaws in pattern recognition. Recognition and classification occur through multiple iterations, incorporating similar and diverse neural features. The learning process is finely tuned for minute classifications using labeled and unlabeled input data. The proposed APCM demonstrates notable achievements, with high pattern recognition (15.03%) and controlled classification errors (CEs) (10.61% less). The method effectively addresses overfitting, robustness, and interoperability issues, showcasing its potential as a powerful tool for detecting neural disorders at different stages. The ability to handle imbalanced data contributes to the overall success of the algorithm. The APCM emerges as a promising and effective approach for identifying precise neural disorder stages. By leveraging AI and ML, the method successfully resolves key challenges in pattern recognition. The high pattern recognition and reduced CEs underscore the method's potential for clinical applications. However, it is essential to acknowledge the reliance on high-quality neural image data, which may limit the generalizability of the approach. The proposed method allows future research to refine further and enhance its interpretability, providing valuable insights into neural disorder progression and underlying biological mechanisms.

DEEP NEURAL MULTI WAVELET SEGMENTATION AND RESNET-50 IMAGE CLASSIFICATION (DNMWS-RESNET-50IC) BASED EARLY GLAUCOMA DETECTION

Glaucoma, a leading cause of irreversible blindness worldwide, results from progressive damage to the optic nerve, often linked to elevated Intra Ocular Pressure (IOP). Early detection is critical for preventing vision loss, yet traditional diagnostic methods can be limited in accessibility and effectiveness, particularly in the early stages of the disease. Addressing the need for early detection, we propose the Deep Neural Multi-Wavelet Segmentation and ResNet-50 Image Classification (DNMWS-ResNet-50IC) method, specifically designed to detect glaucoma at an early stage using retinal fundus images. In this work, we apply Anisotropic Gaussian Filtering for the preprocessing of retinal fundus images, effectively reducing image noise while preserving the original quality of the image pixels by creating an adaptive window size and scale space. We then utilize multi- wavelet-based image segmentation, leveraging wavelet transforms to analyze and decompose the image into its various frequency components. This technique is particularly advantageous for managing images with complex structures and textures. Subsequently, the segmented features are classified using the ResNet-50 model, which categorizes the images as normal, abnormal, or indicative of early- stage glaucoma. The effectiveness of the proposed method is assessed by measuring three key performance indicators sensitivity, specificity, and accuracy on digital retinal images from the HRF image database. Additionally, the model's performance is further evaluated on a separate test set, considering metrics such as accuracy, precision, recall, and prediction time.

Neural image re-exposure

Images and videos often suffer from issues such as motion blur, video discontinuity, or rolling shutter artifacts. Prior studies typically focus on designing specific algorithms to address individual issues. In this paper, we highlight that these issues, albeit differently manifested, fundamentally stem from sub-optimal exposure processes. With this insight, we propose a paradigm termed re-exposure, which resolves the aforementioned issues by performing exposure simulation. Following this paradigm, we design a new architecture, which constructs visual content representation from images and event camera data, and performs exposure simulation in a controllable manner. Experiments demonstrate that, using only a single model, the proposed architecture can effectively address multiple visual issues, including motion blur, video discontinuity, and rolling shutter artifacts, even when these issues co-occur.

Lumpy Skin Disease Detection in Cattle by a Robust Approach using Advanced Convolutional Neural Networks

Background: Lumpy skin disease (LSD) is a significant health concern for cattle globally and poses economic threats by affecting various aspects of cattle health. Integrating artificial intelligence (AI) and machine learning (ML) with visual inspections and biosensor data has shown promise in enhancing disease detection and diagnosis. The present study harnesses the potential of Convolutional Neural Networks (CNN) and image processing for detecting LSD. Methods: Using images from the agricultural landscape, this study highlights the significance of convolutional neural networks that identify the lumpy skin disease (LSD) in animals. Images are categorized into two groups: LSD (infected skin) and non-LSD (normal skin). This is achieved by applying a deeply designed deep learning model carefully built to fulfill this particular need. Evaluation metrics assess the model’s performance, including accuracy, loss and a confusion matrix. Result: A CNN-based model trained for 50 epochs to classify skin conditions, achieved an 86.54% accuracy. The study underscores the potential of CNN in early LSD detection, paving the way for practical applications in veterinary medicine. Future work involves addressing dataset limitations, refining model parameters, reducing image noise, exploring different feature extraction methods and investigating additional animal skin conditions.

Bioinspired Artificial Visual-Respiratory Synapse as Multimodal Scene Recognition System with Oxidized-Vacancies MXene.

In the pursuit of artificial neural systems, the integration of multimodal plasticity, memory retention, and perceptual functions stands as a paramount objective in achieving neuromorphic perceptual components inspired by the human brain, to emulating the neurological excitability tuning observed in human visual and respiratory collaborations. Here, an artificial visual-respiratory synapse is presented with monolayer oxidized MXene (VRSOM) exhibiting synergistic light and atmospheric plasticity. The VRSOM enables to realize facile modulation of synaptic behaviors, encompassing postsynaptic current, sustained photoconductivity, stable facilitation/depression properties, and "learning-experience" behavior. These performances rely on the privileged photocarrier trapping characteristics and the hydroxyl-preferential selectivity inherent of oxidized vacancies. Moreover, environment recognitions and multimodal neural network image identifications are achieved through multisensory integration, underscoring the potential of the VRSOM in reproducing human-like perceptual attributes. The VRSOM platform holds significant promise for hardware output of human-like mixed-modal interactions and paves the way for perceiving multisensory neural behaviors in artificial interactive devices.