Fully Convolutional Neural Network Research Articles

A lightweight deep convolutional network with inverted residuals for matching optical and SAR images

ABSTRACT Matching optical and synthetic aperture radar (SAR) images is often challenged by intricate geometric distortion and nonlinear radiation differences, leading to insufficient and unevenly distributed corresponding points. To tackle this issue, we propose a lightweight deep convolutional network with inverted residuals for optical and SAR image matching. Initially, a fully convolutional neural network (FCNN) is designed to extract high-level and semantic features, robustly capturing universal characteristics between optical and SAR images, adept at handling geometric distortion and nonlinear radiation changes. Notably, we integrate a lightweight architecture with inverted residuals into FCNN to adeptly extract local and global contextual information, facilitating feature reuse and minimizing the loss of crucial features. Additionally, a vector-refined module is deployed to refine dense features, filtering out redundant information. Subsequently, a coarse-to-fine strategy is employed to further eliminate gross errors or incorrect matches. Finally, we evaluate the performance of the proposed network in optical and SAR image matching against manually-designed methods and state-of-the-art deep learning techniques. Experimental results demonstrate that our network significantly surpasses existing methods in terms of the number of correct matches and matching accuracy. Specifically, our proposed network achieves at least a 2.8 times increase in correct matches and an 18% improvement in matching accuracy compared to existing methods.

Generating Multi-Depth 3D Holograms Using a Fully Convolutional Neural Network.

Efficiently generating 3D holograms is one of the most challenging research topics in the field of holography. This work introduces a method for generating multi-depth phase-only holograms using a fully convolutional neural network (FCN). The method primarily involves a forward-backward-diffraction framework to compute multi-depth diffraction fields, along with a layer-by-layer replacement method (L2RM) to handle occlusion relationships. The diffraction fields computed by the former are fed into the carefully designed FCN, which leverages its powerful non-linear fitting capability to generate multi-depth holograms of 3D scenes. The latter can smooth the boundaries of different layers in scene reconstruction by complementing information of occluded objects, thus enhancing the reconstruction quality of holograms. The proposed method can generate a multi-depth 3D hologram with a PSNR of 31.8dB in just 90ms for a resolution of 2160×3840 on the NVIDIA Tesla A100 40G tensor core GPU. Additionally, numerical and experimental results indicate that the generated holograms accurately reconstruct clear 3D scenes with correct occlusion relationships and provide excellent depth focusing.

Automatic Optic Nerve Assessment From Transorbital Ultrasound Images: A Deep Learning-based Approach.

Transorbital Ultrasonography (TOS) is a promising imaging technology that can be used to characterize the structures of the optic nerve and the potential alterations that may occur in those structures as a result of an increase in intracranial pressure (ICP) or the presence of other disorders such as multiple sclerosis (MS) and hydrocephalus. In this paper, the primary objective is to develop a fully automated system that is capable of segmenting and calculating the diameters of structures that are associated with the optic nerve in TOS images. These structures include the optic nerve diameter sheath (ONSD) and the optic nerve diameter (OND). A fully convolutional neural network (FCN) model that has been pre-trained serves as the foundation for the segmentation method. The method that was developed was utilized to collect 464 different photographs from 110 different people, and it was accomplished with the assistance of four distinct pieces of apparatus. An examination was carried out to compare the outcomes of the automatic measurements with those of a manual operator. Both OND and ONSD have a typical inaccuracy of -0.12 0.32 mm and 0.14 0.58 mm, respectively, when compared to the operator. The Pearson correlation coefficient (PCC) for OND is 0.71, while the coefficient for ONSD is 0.64, showing that there is a positive link between the two measuring tools. A conclusion may be drawn that the technique that was developed is automatic, and the average error (AE) that was reached for the ONSD measurement is compatible with the ranges of inter-operator variability that have been discovered in the literature.

Microseismic source location using deep learning: A coal mine case study in China

Microseismic source location is crucial for the early warning of rockburst risks. However, the conventional methods face challenges in terms of the microseismic wave velocity and arrival time accuracy. Intelligent techniques, such as the full convolutional neural network (FCNN), can capture spatial information but struggle with complex microseismic sequence. Combining the FCNN with the long short-term memory (LSTM) network enables better time-series signal classification by integrating multi-scale information and is therefore suitable for waveform location. The LSTM-FCNN model does not require extensive data preprocessing and it simplifies the microseismic source location through feature extraction. In this study, we utilized the LSTM-FCNN as a regression learning model to locate the seismic focus. Initially, the method of short-time-average/long-time-average (STA/LTA) arrival time picking was employed to augment spatiotemporal information. Subsequently, oversampling the on-site data was performed to address the issue of data imbalance, and finally, the performance of LSTM-FCNN was tested. Meanwhile, we compared the LSTM-FCNN model with previous deep-learning models. Our results demonstrated remarkable location capabilities with a mean absolute error (MAE) of only 7.16 m. The model can realize swift training and high accuracy, thereby significantly improving risk warning of rockbursts.

The applicability of automated marine clay gully delineation using deep learning in Norway

AbstractGullies and ravines are common landforms in raised marine fine‐grained deposits in Norway. Gullies in marine clay are significant landforms indicative of soil erosion and natural hazards and are of high conservation value. As a result of the substantial impact of human intervention over the past century, marine clay gullies are now red‐listed. To monitor the condition of these landforms, we need to improve our understanding of their spatial extent, complexity and morphology. We explore the applicability of automated approaches that use a methodology of combining deep learning (DL), fully convolutional neural networks (FCNNs) and an unmodified U‐Net model with ArcPy libraries and ground truth data to derive a high‐resolution map of gullies in raised marine fine‐grained deposits. Predictors used comprise solely terrain derivatives to broaden the usage of the pre‐trained model to other regions. Our best model achieved a precision score of 0.82 and a recall of 0.75. We find that our pre‐trained model can successfully predict gullies, also in blind‐test areas. The model performs better in regions with similar geological settings, scoring a length‐weighted overlap of >70% with reference datasets. The novelty of this study is that we demonstrate that the model's applicability for mapping routines increases when we post‐process the predictions by eliminating noise, especially by using the predictions derived from ensembled models. We, therefore, conclude that the pre‐trained models can effectively be used to supplement the geomorphological mapping of marine clay gullies in Norway. The outcome of this research contributes towards mapping the spatial extent and condition of red‐listed landforms in Norway, as well as the development of monitoring systems for future landscape change.

Dynamic monitoring of surface area and water volume of reservoirs using satellite imagery, computer vision and deep learning

The development of surface area and estimated volume measurements for water reservoirs using advanced computational techniques has become an increasingly important task for reservoir operation. Thus, the objective of this study is to determine the surface area and estimate the volume of water reservoirs through the segmentation of multispectral satellite images using a Fully Convolutional Neural Network (FCN). This paper introduces a universally applicable framework that utilizes deep convolutional neural networks for segmenting water bodies, particularly focusing on areas in Paraíba State, Northeast Brazil, susceptible to severe droughts. The study utilized Sentinel-2 satellite data obtained from Google Earth Engine for the period between January 2019 and September 2023. The extraction of water surface area from satellite images was achieved using the U-Net model, a deep learning framework based on FCN. The accuracy of water surface area extraction was analyzed using the Intersection-Over-Union (IoU) metric, also known as Jaccard Index. Subsequently, reservoir volumes were calculated using elevation-area-volume curves. The evaluation metrics for the results included root mean square error (RMSE), mean absolute percentage error (MAPE), coefficient of determination (R2), and Pearson correlation coefficient (r). The network demonstrated high accuracy, achieving a 95% IoU on the test set. This methodology was applied to four reservoirs, yielding promising results that closely aligned the monitored reservoir curves derived from satellite imagery with observed field data. The findings of this study are significant for research on semiarid region changes, extending their relevance to a broader understanding of global environmental dynamics. This work not only contributes to the field of remote sensing in hydrology but also offers practical insights for effective water resource management in drought-prone regions.

Fault-tolerant Integration of Charge and Discharge Information Security for Multi-Scale Time Series Models

In this paper, the power mode of winding and converter topology are integrated. Considering the filter characteristics and electromagnetic torque control of the system during charging/discharging, the integrated structures of one-phase full bridge, three-phase full bridge and three-phase four-arm bridge are studied. The single-phase all-bridge integrated structure can completely suppress the electromagnetic torque generated by the motor during charging and discharging, and make the filtering effect of the system better. In this project, a multi-scale full convolutional deep neural network method based on time series is studied. The multi-scale full convolution time series model is used for training, and the future continuous signals can be monitored according to the trained model based on the power signal data collected from high-voltage power lines. Experiments show that the proposed method can effectively extract the required data.

Design of an Iterative Method for Enhanced Retinal Image Analysis Using Stacked Deep Learning Operations

The necessity for advanced diagnostic techniques in ophthalmology has become increasingly evident, particularly for conditions such as glaucoma and diabetic retinopathy, which necessitate precise retinal image analysis. Current methodologies, while effective to a degree, fall short in terms of accuracy, efficiency, and adaptability, especially in semantic segmentation and disease classification tasks. These limitations underscore the need for more sophisticated and reliable approaches to retinal image analysis. In response, this work introduces a comprehensive framework leveraging Fully Convolutional Neural Networks (FCNNs) for optic disc segmentation and Cup-to-Disc Ratio (CDR) estimation. The selection of FCNNs is predicated on their demonstrated proficiency in semantic segmentation, ability to discern spatial dependencies, and capacity to learn intricate structures within fundus images without relying on manually engineered features. This approach aims to surpass existing segmentation accuracy benchmarks, targeting over 95% Intersection over Union (IoU) while reducing the mean absolute error in CDR estimation to below 10%. To address the challenges of data variability and quality, the framework incorporates Contrast Limited Adaptive Histogram Equalization (CLAHE) for synthetic data generation, enhancing local contrast while preserving image integrity levels. This method is expected to improve contrast in augmented images by 30%, simultaneously enhancing image quality metrics. Furthermore, the introduction of an overlapping sliding window technique with adaptive patch sizes ensures meticulous coverage and analysis of fundus images, significantly elevating lesion detection sensitivity and reducing false positives. Lastly, the framework employs an innovative multi-disease classification strategy utilizing ensemble learning and stacking of CNN architectures. This synergistic approach amalgamates multiple base models to diminish overfitting risks and enhance generalization capabilities, aiming for a classification accuracy surpassing 95% in binary assessments and 85% for specific retinal diseases. The proposed model not only addresses the current gaps in retinal image analysis but also sets a new standard for precision, reliability, and efficiency in diagnosing and managing ocular diseases, marking a significant step forward in the application of artificial intelligence in ophthalmology.

Segmentation and deep learning to digitalize the kinematics of flow-type landslides

AbstractFlow-type landslides, including subaerial and submarine debris flows, have poor spatiotemporal predictability. Therefore, researchers rely heavily on experimental evidence in revealing complex flow mechanisms and evaluating theoretical models. To measure the velocity field of experimental flows, conventional image analysis tools for measuring soil deformation and hydraulics have been borrowed. However, these tools were not developed for capturing the kinematics of fast-moving soil–water mixtures over complex terrain under non-uniform lighting conditions. In this study, a new framework based on deep learning was used to automatically digitalize the kinematics of experimental flow-type landslides. Captured images were broken into sequences and binarized using a fully convolutional neural network (FCNN). The proposed framework was demonstrated to outperform classic image processing algorithms (e.g., particle image velocimetry, trainable Weka segmentation, and thresholding algorithms) over a wide range of experimental conditions. The FCNN model was even able to process images from consumer-grade cameras under complex shadow, light, and boundary conditions. This feature is most useful for field-scale experimentation. With fewer than 15 annotated training images, the FCNN digitalized experimental flows with an accuracy of 97% in semantic segmentation.

Cross-and-Diagonal Networks: An Indirect Self-Attention Mechanism for Image Classification.

In recent years, computer vision has witnessed remarkable advancements in image classification, specifically in the domains of fully convolutional neural networks (FCNs) and self-attention mechanisms. Nevertheless, both approaches exhibit certain limitations. FCNs tend to prioritize local information, potentially overlooking crucial global contexts, whereas self-attention mechanisms are computationally intensive despite their adaptability. In order to surmount these challenges, this paper proposes cross-and-diagonal networks (CDNet), innovative network architecture that adeptly captures global information in images while preserving local details in a more computationally efficient manner. CDNet achieves this by establishing long-range relationships between pixels within an image, enabling the indirect acquisition of contextual information. This inventive indirect self-attention mechanism significantly enhances the network's capacity. In CDNet, a new attention mechanism named "cross and diagonal attention" is proposed. This mechanism adopts an indirect approach by integrating two distinct components, cross attention and diagonal attention. By computing attention in different directions, specifically vertical and diagonal, CDNet effectively establishes remote dependencies among pixels, resulting in improved performance in image classification tasks. Experimental results highlight several advantages of CDNet. Firstly, it introduces an indirect self-attention mechanism that can be effortlessly integrated as a module into any convolutional neural network (CNN). Additionally, the computational cost of the self-attention mechanism has been effectively reduced, resulting in improved overall computational efficiency. Lastly, CDNet attains state-of-the-art performance on three benchmark datasets for similar types of image classification networks. In essence, CDNet addresses the constraints of conventional approaches and provides an efficient and effective solution for capturing global context in image classification tasks.

Adaptive mask-based brain extraction method for head CT images.

Brain extraction is an important prerequisite for the automated diagnosis of intracranial lesions and determines, to a certain extent, the accuracy of subsequent lesion identification, localization, and segmentation. To address the problem that the current traditional image segmentation methods are fast in extraction but poor in robustness, while the Full Convolutional Neural Network (FCN) is robust and accurate but relatively slow in extraction, this paper proposes an adaptive mask-based brain extraction method, namely AMBBEM, to achieve brain extraction better. The method first uses threshold segmentation, median filtering, and closed operations for segmentation, generates a mask for the first time, then combines the ResNet50 model, region growing algorithm, and image properties analysis to further segment the mask, and finally complete brain extraction by multiplying the original image and the mask. The algorithm was tested on 22 test sets containing different lesions, and the results showed MPA = 0.9963, MIoU = 0.9924, and MBF = 0.9914, which were equivalent to the extraction effect of the Deeplabv3+ model. However, the method can complete brain extraction of approximately 6.16 head CT images in 1 second, much faster than Deeplabv3+, U-net, and SegNet models. In summary, this method can achieve accurate brain extraction from head CT images more quickly, creating good conditions for subsequent brain volume measurement and feature extraction of intracranial lesions.

Pore characterization was achieved based on the improved U-net deep learning network model and scanning electron microscope images

The SEM image method is commonly used in the qualitative characterization of shale pores. The development of shale micro-reservoir pores can be visually observed through SEM images, but the efficiency of manual image processing is low and subjective. The introduction of deep learning greatly improves the efficiency of pore analysis. In this paper, the argon ion polishing SEM image of Longmaxi Formation shale in southern Sichuan is taken as an example. Intelligent identification and quantitative characterization of pores in shale SEM images are realized by Pore-net network model. Pore-net is based on the U-net network model. The way the model reads the data is changed so that the model does not focus on the region of interest. The number of convolutional layers of the model is increased. The Canny edge extraction algorithm is added. It not only reduces the workload of data set production, but also enhances the ability of network model to identify pores. The results show that the deep learning semantic image segmentation method is suitable for pore recognition of shale SEM images. The full convolutional neural network model is used to train the manually labeled shale SEM images, which can realize the intelligent recognition and quantitative characterization of shale SEM images. Compared with FCN and DeepLab V3+ network model, Pore-net performs better. Only 170 data sets are used to train the model. The Pore-net network model still has a good recognition effect on pores, which solves the problem of low accuracy of traditional pore recognition methods. The deviation between the porosity calculated by the Pore-net network model and the experimental data is between 12% and 19%. Compared with the porosity results calculated by the binarization method and other network models, the results calculated by Pore-net are closer to the real values, which proves that the porosity calculated by the Pore-net network model is reliable.

Pelvic bone tumor segmentation fusion algorithm based on fully convolutional neural network and conditional random field

Background and objectivePelvic bone tumors represent a harmful orthopedic condition, encompassing both benign and malignant forms. Addressing the issue of limited accuracy in current machine learning algorithms for bone tumor image segmentation, we have developed an enhanced bone tumor image segmentation algorithm. This algorithm is built upon an improved full convolutional neural network, incorporating both the fully convolutional neural network (FCNN-4s) and a conditional random field (CRF) to achieve more precise segmentation. MethodologyThe enhanced fully convolutional neural network (FCNN-4s) was employed to conduct initial segmentation on preprocessed images. Following each convolutional layer, batch normalization layers were introduced to expedite network training convergence and enhance the accuracy of the trained model. Subsequently, a fully connected conditional random field (CRF) was integrated to fine-tune the segmentation results, refining the boundaries of pelvic bone tumors and achieving high-quality segmentation. ResultsThe experimental outcomes demonstrate a significant enhancement in segmentation accuracy and stability when compared to the conventional convolutional neural network bone tumor image segmentation algorithm. The algorithm achieves an average Dice coefficient of 93.31 %, indicating superior performance in real-time operations. ConclusionIn contrast to the conventional convolutional neural network segmentation algorithm, the algorithm presented in this paper boasts a more intricate structure, proficiently addressing issues of over-segmentation and under-segmentation in pelvic bone tumor segmentation. This segmentation model exhibits superior real-time performance, robust stability, and is capable of achieving heightened segmentation accuracy.

MT2DInv-Unet: A 2D magnetotelluric inversion method based on deep-learning technology

Traditional gradient-based inversion methods usually suffer from the problems of falling into local minima and relying heavily on initial guesses. Deep-learning methods have received increasing attention due to their excellent nonlinear fitting ability. However, given the recent application of deep-learning methods in the field of magnetotelluric (MT) inversion, there are currently challenges associated with achieving high inversion resolution and extracting sufficient features. We develop a neural network model (called MT2DInv-Unet) based on the deformable convolution for 2D MT inversion to approximate the nonlinear mapping from the MT response data to the resistivity model. The deformable convolution is achieved by adding an offset to each sample point of the conventional convolution operation, which extracts hidden relationships and allows the flexible adjustment of the size and shape of the feature region. Meanwhile, we design the network structure with multiscale residual blocks, which effectively extract the multiscale features of the MT response data. This design not only enhances the network performance but also alleviates issues such as vanishing gradients and network degradation. The results of synthetic models indicate that our network inversion method has stable convergence, good robustness, and generalization performance, and it performs better than the fully convolutional neural network and U-Net network. Finally, the inversion results of field data show that MT2DInv-Unet can effectively obtain a reliable underground resistivity structure and has a good application prospect in MT inversion.

Recoding double-phase holograms with the full convolutional neural network

Herein, we proposed a method to recode double-phase holograms (DPHs) with a full convolutional neural network (FCN) for alleviating the fringes and spatial shifting noises in that. There are many fringes near the edge of the diffraction field due to the incomplete Fresnel zone plate, and the pixel-by-pixel encoding method of DPH will cause above fringes to appear in the reconstructed image of that as well. Besides, the spatial shifting noises generated during the conversion from complex-amplitude information to pure-phase information will also decrease the reconstruction quality and definition. Depending on the great nonlinear fitting ability of FCN, the proposed method can recode DPHs, alleviating the fringes and removing the spatial shifting noises effectively. Finally, the phase-only holograms with the resolution 2400 × 4096 have been generated in 0.06 s, which have a peak signal to noise ratio (PSNR) of 35.6 dB in the mean reconstruction quality. Optical results showed that compared with the conventional methods, the target images reconstructed by the proposed method have less noise and a higher display quality.

Multi-network approach for image segmentation in non-contrast enhanced cardiac 3D MRI of arrhythmic patients

Left atrial appendage (LAA) is the source of thrombi formation in more than 90% of strokes in patients with nonvalvular atrial fibrillation. Catheter-based LAA occlusion is being increasingly applied as a treatment strategy to prevent stroke. Anatomical complexity of LAA makes percutaneous occlusion commonly performed under transesophageal echocardiography (TEE) and X-ray (XR) guidance especially challenging. Image fusion techniques integrating 3D anatomical models derived from pre-procedural imaging into the live XR fluoroscopy can be applied to guide each step of the LAA closure. Cardiac magnetic resonance (CMR) imaging gains in importance for radiation-free evaluation of cardiac morphology as alternative to gold-standard TEE or computed tomography angiography (CTA). Manual delineation of cardiac structures from non-contrast enhanced CMR is, however, labor-intensive, tedious, and challenging due to the rather low contrast. Additionally, arrhythmia often impairs the image quality in ECG synchronized acquisitions causing blurring and motion artifacts. Thus, for cardiac segmentation in arrhythmic patients, there is a strong need for an automated image segmentation method. Deep learning-based methods have shown great promise in medical image analysis achieving superior performance in various imaging modalities and different clinical applications. Fully-convolutional neural networks (CNNs), especially U-Net, have become the method of choice for cardiac segmentation. In this paper, we propose an approach for automatic segmentation of cardiac structures from non-contrast enhanced CMR images of arrhythmic patients based on CNNs implemented in a multi-stage pipeline. Two-stage implementation allows subdividing the task into localization of the relevant cardiac structures and segmentation of these structures from the cropped sub-regions obtained from previous step leading to efficient and effective way of automated cardiac segmentation.

Evaluation of brain age changes in patients with liver cirrhosis and hepatic encephalopathy with deep learning models based on structural magnetic resonance imaging

Objective: To investigate the brain aging in patients with cirrhosis and hepatic encephalopathy(HE), constructed a prediction model of brain age based on deep learning and T1 high-resolution MRI, and try to reveal the specific regions where cirrhosis and HE accelerating brain aging. Methods: A cross-sectional study. A brain age prediction model based on the 3D full convolutional neural network was constructed through T1 high-resolution MRI data from 3 609 healthy individuals across eight global public datasets. The mean absolute error (MAE) between actual age and predicted brain age, Pearson correlation coefficient (r) and determination coefficient (R2) were calculated to evaluate the accuracy of the model's predictions. A test set (n=555) from the Human Connectome Project was used to assess the accuracy of the model. A total of 136 patients with cirrhosis were recruited from Tianjin First Central Hospital as the case group (79 patients with cirrhosis without HE and 57 patients with cirrhosis with HE), and 70 healthy individuals were recruited from the society as the healthy control group during the same period. Brain-predicted age difference (Brain-PAD), digital connection-A (NCT-A) and digital-symbol test (DST) scores of all subjects were calculated for all subjects to assess brain aging and cognitive function in the healthy control group, the cirrhosis without HE group, and the cirrhosis with HE group. The network occlusion sensitivity analysis method was employed to assess the importance of each brain region in predicting brain age. Results: As for the prediction model, in the training set, MAE=2.85, r=0.98, R2=0.96. In the test set, MAE=4.45, r=0.96, R2=0.92. In the local data set of the healthy control group, MAE=3.77, r=0.85, R2=0.73. The time of NCT-A in both cirrhosis groups was longer than healthy control group, while the DST scores were lower than healthy control group, and the differences were statistically significant (both P<0.001); the Brain-PAD of healthy control group was (0.8±4.5) years, the Brain-PAD of no-HE group was (6.9±8.1) years, and the HE group was (10.2±7.7) years. The differences between the three groups were statistically significant (P<0.001), and the differences between any two groups were statistically significant (all P<0.05). The importance ratio of visual network in predicting brain age increased in cirrhosis patients, and the HE group was higher than no-HE group. Conclusions: In patients with cirrhosis, the cognitive function is reduced, brain aging is accelerated, and these changes are more obvious in patients with HE. The importance differences of each brain network in predicting brain aging provide a new direction for identifying the specific regions where cirrhosis and HE accelerate brain aging.

Cardiac abnormalities from 12‐Lead ECG signals prediction based on deep convolutional neural network optimized with nomadic people optimization algorithm

SummaryCardiovascular disease (CVD) is a most dangerous disease in the world. Early accurate and automated identification helps the medical professional make a correct diagnosis and administer fast treatment and saving many lives. Several studies have been suggested in this area, but no one yield the expected outcomes owing to data imbalance issue in the medical and healthcare industries. To overcome this problem, a Deep Convolutional Neural Network Optimized with Nomadic People Optimization for Cardiac Abnormalities from 12‐Lead ECG Signals Prediction (CCA‐12L ECG‐DCNN‐NPO) is proposed in this manuscript. At first, the input data is pre‐processed under Morphological filtering and Extended Empirical wavelet transformation (MF‐EEWT) for removing the noise. Then one hot encoding technique is used to improve the predictions and classification accuracy of the method. Afterward, Residual Exemplars Local Binary Pattern (RELBP) based Feature extraction is used to extract the morphological and statistical features. These extracted features are given to DCNN classifier. It contains fully convolutional neural network (FCN) and encoder with decoder framework, which activates pixel‐wise categorization to exactly identify Cardiac abnormalities from 12‐Lead ECG signals. The visual geometry group network (VGGNet) is considered as a backbone of FCN for end‐to‐end training. Generally, DCNN method does not adopt any optimization modes to define the optimum parameters and to assure exact detection. Therefore, Nomadic People Optimization (NPO) is considered to enhance the DCNN weight parameters. The CCA‐12L ECG‐DCNN‐NPO technique is implemented in python and the efficacy is analyzed under performance metrics, such as sensitivity, precision, F‐Score, specificity, accuracy and error rate. From the analysis, the proposed technique attains higher accuracy 27.5%, 10.32%, and 16.65%, higher f‐score 30.93%, 11.14% and 15.3%, lower error rate 36.31%, 15.78%, and 28.08% compared with the existing methods, such as Detecting Cardiac Abnormalities from 12‐lead ECG Signals Under Feature Selection, Feature Extraction, and deep Learning Classification (CCA‐12L ECG‐RFC), Channel self‐attention deep learning framework for multi‐cardiac abnormality diagnosis from varied‐lead ECG signals (CCA‐12L ECG‐CSA‐DNN) and Cardiac disease categorization by electrocardiogram sensing utilizing deep neural network (CCA‐12L ECG‐DNN) respectively.

A brain tumor identification using convolution neural network and fully convolution neural network

Brain tumor identification, along with an investigation, is harmful to the patient. Segmentation, therefore, of paying attention to near-neighborhood growth remains accurate, effective, and healthy. Fully Convolution Neural Network (FCNN) is a reliable picture model to capitulate the hide quality. The form of the multifaceted with the incessant pixels taught with the crest state and the symbolic picture taught. In this research, the making of a totally convoluted method to obtain the participation of a random element and the production of correspondingly large-scale output with a resourceful assumption and information.. The approach has had several difficulties, as measurements are accurate for a variety of images. The improvement in the mortality rate of the programmed order is a critical condition. The scheduling of the mind tumor is an exceedingly troublesome task in the exceptional spatial and basic fluctuation that accompanies the local brain tumor. In this research, a programmed detection of Brain tumors proposed using the characterization of CNN. The most critical method of construction is the completion of the use of small holes. CNN's has less predictability and 97.5 accuracies.

Deep reinforcement learning with light-weight vision model for sequential robotic object sorting

Training an agent to generate cooperative joint learning policies for executing object sorting in cluttered scenes is complex. This paper presents a comparative model-free deep reinforcement learning (DRL) system built from variants of end-to-end lightweight deep neural networks that jointly complement three primitive policies (pushing, grasping, and placing) that operate from visual observations to actions. The end goal of the proposed DRL systems is to perform object sorting in very dynamic and complex environmental scenarios of cluttered and random objects (irregular and regular blocks) existing in different color variations. For this, we investigated DRL methods built from 12 custom instances of Pixel-wise Q-valued Critic Networks (PQCN) from four main backbone networks (DenseNet121, DenseNet169, MobileNetV3, and SqueezeNet) individually combined with custom fully convolutional neural networks (FCN) for learning the image-based observation space through affordance mapping paradigm while considering both dual and single transfer learning schemes. Additionally, we explored three categories of gradient-based optimization methods and considered custom reward functions with varying discount factors. The results show that the PQCN constructed by integrating DenseNet121 or MobileNetV3 with FCN and trained by a dual transfer learning scheme (with a discount factor of γ=0.70) yielded the best performance in most categories of our evaluation in the training phases. Overall, the PQCN-DenseNet121, when trained with a dual transfer learning process, yielded the best generalization (sorting success rate) in most of the evaluation metrics across all object categories in the testing phase. Furthermore, our study provides a new benchmark for validating deep reinforcement learning algorithms while sorting object blocks under varying degrees of complexity. This research finds practical relevance in industrial fields such as manufacturing and packaging, building construction, plant sorting, automobile assembly lines, and waste sorting. Here is the article’s source code: https://github.com/Emmanuel-Okafor/DRL_for_Object_Sorting.git.