Features Of Original Image Research Articles

Qualitative Analysis of Cryptanalysis of Images Encrypted by Border Chaotic Cellular-Automaton-Based Model Using Segmentation Techniques

Data transactions on computers and phones have become increasingly common, with numerous entities exchanging both public and private data across expanding networks. However, for sensitive data, such as private information, transmitting it over public access networks like the Internet may pose security risks. Digital images are a particularly representative and increasingly significant data type in this context. To safeguard such data from leaks and unauthorized alterations during transmission, encryption serves as a crucial measure. Various cryptographic algorithms are available, each with distinct security requirements. Among these, encryption algorithms based on Cellular Automata offer alternative solutions characterized by their highly chaotic evolutionary capabilities, providing a significant degree of obfuscation, a critical aspect for maintaining confidentiality. This study employs image segmentation techniques such as k-means clustering, edge detection, and thresholding as tools for cryptanalysis. These segmentation techniques are applied to digital images generated using the Border Chaotic Cellular Automata (BCCA) cryptographic model. This approach enables the identification of artifacts and the highlighting of original image features within low-quality encryption. Conversely, more robustly encrypted images, achieved through additional encryption steps, remain unaffected by these techniques, underscoring the level of confidentiality inherent in the BCCA model.

TPAFNet: Transformer-Driven Pyramid Attention Fusion Network for 3D Medical Image Segmentation.

The field of 3D medical image segmentation is witnessing a growing trend in the utilization of combined networks that integrate convolutional neural networks and transformers. Nevertheless, prevailing hybrid networks are confronted with limitations in their straightforward serial or parallel combination methods and lack an effective mechanism to fuse channel and spatial feature attention. To address these limitations, we present a robust multi-scale 3D medical image segmentation network, the Transformer-Driven Pyramid Attention Fusion Network, which is denoted as TPAFNet, leveraging a hybrid structure of CNN and transformer. Within this framework, we exploit the characteristics of atrous convolution to extract multi-scale information effectively, thereby enhancing the encoding results of the transformer. Furthermore, we introduce the TPAF block in the encoder to seamlessly fuse channel and spatial feature attention from multi-scale feature inputs. In contrast to conventional skip connections that simply concatenate or add features, our decoder is enriched with a TPAF connection, elevating the integration of feature attention between low-level and high-level features. Additionally, we propose a low-level encoding shortcut from the original input to the decoder output, preserving more original image features and contributing to enhanced results. Finally, the deep supervision is implemented using a novel CNN-based voxel-wise classifier to facilitate better network convergence. Experimental results demonstrate that TPAFNet significantly outperforms other state-of-the-art networks on two public datasets, indicating that our research can effectively improve the accuracy of medical image segmentation, thereby assisting doctors in making more precise diagnoses.

Identifying fine-scale archaeological features using KH-9 HEXAGON mapping and panoramic camera images: evidence from Liangzhu Ancient City

ABSTRACT Historical fine-scale information of archaeological landscapes is crucial in archaeological investigations. However, documenting such information using satellite sensor data prior to 2000 remains a daunting challenge. Images from the declassified archives of KH-9 HEXAGON (KH-9) cameras, including the panoramic camera system (PCS) and mapping camera system (MCS), offer fine-scale information about archaeological sites. However, noise, contrast distortion and the availability of only a single panchromatic band can limit their potential, particularly for identifying features in subtropical climates within heterogeneous landscape types. This paper focuses on developing a novel multifaceted analytical framework with two components: image pre-processing and feature identification. The image pre-processing component is divided into two steps. First, a trained stationary wavelet transform (SWT) based on the normalized sill (NS) is developed to not only de-noises the image, but also preserve its original image characteristics. Then, the contrast of the de-noised images is optimized by the multi-resolution Top-hat (MTH) using multi-scale information. In the feature identification component, the MCS image is analysed using spatial colour composite write function memory (SCCWFM) and spatial novelty detection (SND). An ultra-fine spatial three-dimensional colour composite (UFSTCC) image and ultra-fine spatial digital surface model (UFSDSM) are produced to aid interpretation of the KH-9 PCS images. The proposed processing pipelines were tested on KH-9 MCS and PCS images of the World Heritage site at Liangzhu Ancient City (LAC) in China, which is characterized by a subtropical climate and a heterogeneous landscape types. The proposed pre-processing pipeline improved considerably the appearance of these images across the LAC landscape while maintaining the original image information. The developed digital analytical approaches for KH-9 PCS and MCS images facilitated straightforward identification of archaeological features in the LAC. The proposed framework has the potential to increase exploitation of the available KH-9 images in archaeological applications.

Multi-step framework for glaucoma diagnosis in retinal fundus images using deep learning.

Glaucoma is one of the most common causes of blindness in the world. Screening glaucoma from retinal fundus images based on deep learning is a common method at present. In the diagnosis of glaucoma based on deep learning, the blood vessels within the optic disc interfere with the diagnosis, and there is also some pathological information outside the optic disc in fundus images. Therefore, integrating the original fundus image with the vessel-removed optic disc image can improve diagnostic efficiency. In this paper, we propose a novel multi-step framework named MSGC-CNN that can better diagnose glaucoma. In the framework, (1) we combine glaucoma pathological knowledge with deep learning model, fuse the features of original fundus image and optic disc region in which the interference of blood vessel is specifically removed by U-Net, and make glaucoma diagnosis based on the fused features. (2) Aiming at the characteristics of glaucoma fundus images, such as small amount of data, high resolution, and rich feature information, we design a new feature extraction network RA-ResNet and combined it with transfer learning. In order to verify our method, we conduct binary classification experiments on three public datasets, Drishti-GS, RIM-ONE-R3, and ACRIMA, with accuracy of 92.01%, 93.75%, and 97.87%. The results demonstrate a significant improvement over earlier results.

Multi-scale object equalization learning network for intracerebral hemorrhage region segmentation

Segmentation and the subsequent quantitative assessment of the target object in computed tomography (CT) images provide valuable information for the analysis of intracerebral hemorrhage (ICH) pathology. However, most existing methods lack a reasonable strategy to explore the discriminative semantics of multi-scale ICH regions, making it difficult to address the challenge of complex morphology in clinical data. In this paper, we propose a novel multi-scale object equalization learning network (MOEL-Net) for accurate ICH region segmentation. Specifically, we first introduce a shallow feature extraction module (SFEM) for obtaining shallow semantic representations to maintain sufficient and effective detailed location information. Then, a deep feature extraction module (DFEM) is leveraged to extract the deep semantic information of the ICH region from the combination of SFEM and original image features. To further achieve equalization learning in different scales of ICH regions, we introduce a multi-level semantic feature equalization fusion module (MSFEFM), which explores the equalized fusion features of the described objects with the assistance of shallow and deep semantic information provided by SFEM and DFEM. Driven by the above three designs, MOEL-Net shows a solid capacity to capture more discriminative features in various ICH region segmentation. To promote the research of clinical automatic ICH region segmentation, we collect two datasets, VMICH and FRICH (divided into Test A and Test B) for evaluation. Experimental results show that the proposed model achieves the Dice scores of 88.28%, 90.92%, and 90.95% on the VMICH, FRICH Test A, and Test B, respectively, which outperform fourteen competing methods.

Hyperspectral image classification based on deep separable residual attention network

Hyperspectral image have rich spatial and spectral information, and how to fully extract and utilize the features of these two dimensions is a research hotspot in hyperspectral classification methods. At present, the unique convolutional operation and deep feature extraction structure of convolutional neural network enable them to have stronger feature representation capabilities and achieve good results in hyperspectral image classification. However, CNN methods do not assign different weights based on the importance of features in the feature extraction process, making it difficult to effectively utilize key features, and most importantly, using fixed shaped convolution kernel can easily overlook the differences between hyperspectral image features. A hyperspectral image classification method based on deep separable residual attention network is proposed to address the above issues. Firstly, to reduce the correlation between hyperspectral image data and minimize the interference of redundant information, principal component analysis is used to reduce the dimensionality of hyperspectral image. Secondly, a shallow feature extraction module is constructed, which can dynamically adjust the size of the receptive field according to the actual situation of the image, adaptively extract shallow features, and reduce the loss of original image features. Then, a depthwise separable residual attention mechanism module is proposed, based on which features are extracted. Starting from global and local features, contextual information on image features in channel and spatial domains is extracted. Finally, use a multi-scale feature fusion module to fully integrate feature maps at different scales. Using Indian Pines, Pavia University and Botswana as experimental datasets, the overall classification accuracy of this paper's method is 98.47 %, 98.70 %, 98.83 % with only 50, 50, 30 training samples per class. The Kappa coefficient is 98.25 %, 98.27 %, and 98.73 %, respectively. Compared with advanced methods, this method not only has higher classification accuracy, but also fully utilizes key features at various network levels.

Zero-shot denoising of microscopy images recorded at high-resolution limits.

Conventional and electron microscopy visualize structures in the micrometer to nanometer range, and such visualizations contribute decisively to our understanding of biological processes. Due to different factors in recording processes, microscopy images are subject to noise. Especially at their respective resolution limits, a high degree of noise can negatively effect both image interpretation by experts and further automated processing. However, the deteriorating effects of strong noise can be alleviated to a large extend by image enhancement algorithms. Because of the inherent high noise, a requirement for such algorithms is their applicability directly to noisy images or, in the extreme case, to just a single noisy image without a priori noise level information (referred to as blind zero-shot setting). This work investigates blind zero-shot algorithms for microscopy image denoising. The strategies for denoising applied by the investigated approaches include: filtering methods, recent feed-forward neural networks which were amended to be trainable on noisy images, and recent probabilistic generative models. As datasets we consider transmission electron microscopy images including images of SARS-CoV-2 viruses and fluorescence microscopy images. A natural goal of denoising algorithms is to simultaneously reduce noise while preserving the original image features, e.g., the sharpness of structures. However, in practice, a tradeoff between both aspects often has to be found. Our performance evaluations, therefore, focus not only on noise removal but set noise removal in relation to a metric which is instructive about sharpness. For all considered approaches, we numerically investigate their performance, report their denoising/sharpness tradeoff on different images, and discuss future developments. We observe that, depending on the data, the different algorithms can provide significant advantages or disadvantages in terms of their noise removal vs. sharpness preservation capabilities, which may be very relevant for different virological applications, e.g., virological analysis or image segmentation.

Research on the generation and annotation method of thin section images of tight oil reservoir based on deep learning

The cast thin sections of tight oil reservoirs contain important parameters such as rock mineral composition and content, porosity, permeability and stratigraphic characteristics, which are of great significance for reservoir evaluation. The use of deep learning technology for intelligent identification of thin section images is a development trend of mineral identification. However, the difficulty of making cast thin sections, the complexity of the making process and the high cost of thin section annotation have led to a lack of cast thin section images, which cannot meet the training requirements of deep learning image recognition models. In order to increase the sample size and improve the training effect of deep learning model, we proposed a generation and annotation method of thin section images of tight oil reservoir based on deep learning, by taking Fuyu reservoir in Sanzhao Sag as the target area. Firstly, the Augmentor strategy space was used to preliminarily augment the original images while preserving the original image features to meet the requirements of the model. Secondly, the category attention mechanism was added to the original StyleGAN network to avoid the influence of the uneven number of components in thin sections on the quality of the generated images. Then, the SALM annotation module was designed to achieve semi-automatic annotation of the generated images. Finally, experiments on image sharpness, distortion, standard accuracy and annotation efficiency were designed to verify the advantages of the method in image quality and annotation efficiency.

CenterADNet: Infrared Video Target Detection Based on Central Point Regression.

Infrared video target detection is a fundamental technology within infrared warning and tracking systems. In long-distance infrared remote sensing images, targets often manifest as circular spots or even single points. Due to the weak and similar characteristics of the target to the background noise, the intelligent detection of these targets is extremely complex. Existing deep learning-based methods are affected by the downsampling of image features by convolutional neural networks, causing the features of small targets to almost disappear. So, we propose a new infrared video weak-target detection network based on central point regression. We focus on suppressing the image background by fusing the different features between consecutive frames with the original image features to eliminate the background's influence. We also employ high-resolution feature preservation and incorporate a spatial-temporal attention module into the network to capture as many target features as possible and improve detection accuracy. Our method achieves superior results on the infrared image weak aircraft target detection dataset proposed by the National University of Defense Technology, as well as on the simulated dataset generated based on real-world observation. This demonstrates the efficiency of our approach for detecting weak point targets in infrared continuous images.

ALIKE-APPLE: A Lightweight Method for the Detection and Description of Minute and Similar Feature Points in Apples

Current image feature extraction methods fail to adapt to the fine features of apple image texture, resulting in image matching errors and degraded image processing accuracy. A multi-view orthogonal image acquisition system was constructed with apples as the research object. The system consists of four industrial cameras placed around the apple at different angles and one camera placed on top. Following the image acquisition through the system, synthetic image pairs—both before and after transformation—were generated as the input dataset. This generation process involved each image being subjected to random transformations. Through learning to extract more distinctive and descriptive features, the deep learning-based keypoint detection method surpasses traditional techniques by broadening the application range and enhancing detection accuracy. Therefore, a lightweight network called ALIKE-APPLE was proposed for surface feature point detection. The baseline model for ALIKE-APPLE is ALIKE, upon which improvements have been made to the image feature encoder and feature aggregation modules. It comprises an Improved Convolutional Attention Module (ICBAM) and a Boosting Resolution Sampling Module (BRSM). The proposed ICBAM replaced max pooling in the original image feature encoder for downsampling. It enhanced the feature fusion capability of the model by utilizing spatial contextual information and learning region associations in the image. The proposed BRSM replaced the bilinear interpolation in the original feature aggregator for upsampling, overcoming the apple side image’s geometric distortion and effectively preserving the texture details and edge information. The model size was shrunk by optimizing the number of downsampling operations from the image encoder of the original model. The experimental results showed that the average number of observed keypoints and the average matching accuracy were improved by 166.41% and 37.07%, respectively, compared with the baseline model. The feature detection model of ALIKE-APPLE was found to perform better than the optimal SuperPoint. The feature point distribution of ALIKE-APPLE showed an improvement of 10.29% in average standard deviation (Std), 8.62% in average coefficient of variation (CV), and 156.12% in average feature point density (AFPD). Moreover, the mean matching accuracy (MMA) of ALIKE-APPLE improved by 125.97%. Thus, ALIKE-APPLE boasts a more consistent allocation of feature points and greater precision in matching.

TBACkp: HER2 expression status classification network focusing on intrinsic subenvironmental characteristics of breast cancer liver metastases

The HER2 expression status in breast cancer liver metastases is a crucial indicator for the diagnosis, treatment, and prognosis assessment of patients. And typical diagnosis involves assessing the HER2 expression status through invasive procedures like biopsy. However, this method has certain drawbacks, such as being difficult in obtaining tissue samples and requiring long examination periods. To address these limitations, we propose an AI-aided diagnostic model. This model enables rapid diagnosis. It diagnoses a patient's HER2 expression status on the basis of preprocessed images, which is the region of the lesion extracted from a CT image rather than from an actual tissue sample. The algorithm of the model adopts a parallel structure, including a Branch Block and a Trunk Block. The Branch Block is responsible for extracting the gradient characteristics between the tumor sub-environments, and the Trunk Block is for fusing the characteristics extracted by the Branch Block. The Branch Block contains CNN with self-attention, which combines the advantages of CNN and self-attention to extract more meticulous and comprehensive image features. And the Trunk Block is so designed that it fuses the extracted image feature information without affecting the transmission of the original image features. The Conv-Attention is used to calculate the attention in the Trunk Block, which uses kernel dot product and is responsible for providing the weight for the self-attention in the process of using convolution induced deviation calculation. Combined with the structure of the model and the method used, we refer to this model as TBACkp. The dataset comprises the enhanced abdominal CT images of 151 patients with liver metastases from breast cancer, together with the corresponding HER2 expression levels for each patient. The experimental results are as follows: (AUC: 0.915, ACC: 0.854, specificity: 0.809, precision: 0.863, recall: 0.881, F1-score: 0.872). The results demonstrate that this method can accurately assess the HER2 expression status in patients when compared with other advanced deep learning model.

CNN-based multi-modal radiomics analysis of pseudo-CT utilization in MRI-only brain stereotactic radiotherapy: a feasibility study

BackgroundPseudo-computed tomography (pCT) quality is a crucial issue in magnetic resonance image (MRI)-only brain stereotactic radiotherapy (SRT), so this study systematically evaluated it from the multi-modal radiomics perspective.Methods34 cases (< 30 cm³) were retrospectively included (2021.9-2022.10). For each case, both CT and MRI scans were performed at simulation, and pCT was generated by a convolutional neural network (CNN) from planning MRI. Conformal arc or volumetric modulated arc technique was used to optimize the dose distribution. The SRT dose was compared between pCT and planning CT with dose volume histogram (DVH) metrics and gamma index. Wilcoxon test and Spearman analysis were used to identify key factors associated with dose deviations. Additionally, original image features were extracted for radiomic analysis. Tumor control probability (TCP) and normal tissue complication probability (NTCP) were employed for efficacy evaluation.ResultsThere was no significant difference between pCT and planning CT except for radiomics. The mean value of Hounsfield unit of the planning CT was slightly higher than that of pCT. The Gadolinium-based agents in planning MRI could increase DVH metrics deviation slightly. The median local gamma passing rates (1%/1 mm) between planning CTs and pCTs (non-contrast) was 92.6% (range 63.5–99.6%). Also, differences were observed in more than 85% of original radiomic features. The mean absolute deviation in TCP was 0.03%, and the NTCP difference was below 0.02%, except for the normal brain, which had a 0.16% difference. In addition, the number of SRT fractions and lesions, and lesion morphology could influence dose deviation.ConclusionsThis is the first multi-modal radiomics analysis of CNN-based pCT from planning MRI for SRT of small brain lesions, covering dosiomics and radiomics. The findings suggest the potential of pCT in SRT plan design and efficacy prediction, but caution needs to be taken for radiomic analysis.

A Finger Vein Recognition Algorithm Based on the Histogram of Variable Curvature Directional Binary Statistics

Structural features are capable of effectively capturing the overall texture variations in images. However, in locally prominent areas with visible veins, other characteristics such as directionality, convexity–concavity, and curvature also play a crucial role in recognition, and their impact cannot be overlooked. This paper introduces a novel approach, the histogram of variable curvature directional binary statistical (HVCDBS), which combines the structural and directional features of images. The proposed method is designed for extracting discriminative multifeature information in vein recognition. First, a multidirection and multicurvature Gabor filter is introduced for convolution with vein images, yielding directional and convexity–concavity information at each pixel, along with curvature information for the corresponding curve. Simultaneously incorporating the original image feature information, these four aspects of information are fused and encoded to construct a variable curvature binary pattern (VCBP) with multifeatures. Second, the feature map containing multifeature information is blockwise processed to build variable curvature binary statistical features. Finally, competitive Gabor directional binary statistical features are combined, and a matching score‐level fusion scheme is employed based on maximizing the interclass distance and minimizing the intraclass distance to determine the optimal weights. This process fuses the two feature maps into a one‐dimensional feature vector, achieving an effective representation of vein images. Extensive experiments were conducted on four widely utilized vein databases, and the results indicate that the proposed algorithm, compared with solely extraction of structural features, achieved higher recognition rates and lower equal error rates.

Sketch to Image Translation using Generative Adversarial Network

Using a Generative Adversarial Network (GAN) has proven its ability to successfully implement realistic images in image translation fields. It has its successful implementation in the sketch-to-image translation, too. Generative adversarial networks are widely used for the purpose of image translation. Most discriminators in generative adversarial networks use encoder or decoder blocks for image segmentation and classification tasks. U-net-based architecture is mostly used in the generator but rarely in the discriminator. If used in the discriminator, it is used for image resolution increment and segmentation tasks. In this research, a U-net-based discriminator is used for image translation tasks. U-net-based discriminator uses local and global differences between the real and fake images, which helps maintain global and local data representation. Resnet-9, used in the generator, uses skip connections, shortcuts, and concatenations, enabling information to flow from earlier to later layers. This helps preserve the original image features and solves the vanishing gradient problems in normal generators. The use of a strong discriminator and effective generator helps in the improvement system's performance. The available dataset was unpaired at the same time. Datasets from various sources were combined and formed a sketch-image pair. The input is a 512x256 human sketch and a corresponding real image pair. The image pair is split into sketch and image with dimensions 256x256. The system's output is the human face image of the corresponding sketch.

Attention-Rectified and Texture-Enhanced Cross-Attention Transformer Feature Fusion Network for Facial Expression Recognition

Facial Expression Recognition (FER) in the wild is a challenging task for affective computing in human-machine interaction fields. However, most of existing methods fail to learn the most prominent regions of facial images by simple cross-entropy loss due to the imbalance problem commonly existing in FER datasets, which limits the robustness and interpretability of the model. In addition, these methods only capture local features of original images with multi-size shallow convolution and ignore facial texture characteristics, leading to a suboptimal recognition performance. To address these issues, we propose a novel facial expression recognition network, named the attention-rectified and texture-enhanced cross-attention transformer feature fusion network (AR-TE-CATFFNet). Specifically, an attention-rectified convolution block is first designed to assist multiple convolution heads to focus on critical areas of human faces and improve the model generalization. Second, we investigate a texture enhancement block to capture texture features through local binary pattern and gray-level co-occurrence matrix, which solves the limitation of insufficient texture information. Finally, a cross-attention transformer feature fusion block is employed to deeply integrate RGB features and texture features globally, which is beneficial to boost the accuracy of recognition. Competitive experimental results on three public datasets validate the efficacy of the proposed method, indicating that our proposed method achieves superior classification performance of 89.50% on RAF-DB dataset, 65.66% on AffectNet dataset, and 74.84% on FER2013 dataset against the existing methods. The code of our proposed method will be available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/smy17/AR-TE-CATFFNet</uri> .

Myocardial Segmentation Algorithm of U-Net Network Based on Cardiac Ultrasound Images

The myocardial state is always regarded as an important basis for identifying cardiac diseases. In order to assist physicians in diagnosis in an accurate manner, this paper proposes myocardial segmentation using a U-Net network based on cardiac ultrasound images. Firstly, we collected a large amount of clinical data and employed professional cardiac ultrasound imaging physicians to mark the myocardial regions as the gold standard. Then, we built an optimized U-Net network to establish the relationship between images and semantics to extract original image features. Finally, a newly fused loss function for training the network is created. According to the experiments, it shows that the accuracy, precision, and recall rate of U-Net indexes proposed in this paper reaches more than 96%, and MIOU more than 94%, which can effectively assist doctors in diagnosis in an accurate manner.

A novel color image encryption scheme using elliptic curve cryptography and hyperchaotic system

This paper develops an asymmetric color image encryption algorithm based on elliptic curve cryptography(ECC), five dimensions(5D) hyperchaotic system, and DNA dynamic coding. To embed the characteristics of original image in the image encryption algorithm, this algorithm builds a mathematical model to strengthen the connection between the original image, elliptic curve Diffie-Hellman(ECDH) algorithm and hyperchaotic system. The red, green and blue(RGB) channels of encrypted image is reshaped into a three dimensions(3D) matrix. Grouping and scrambling of 3D matrix is accomplished at pixel level, bit level and DNA level based on a 5D hyperchaotic system, which effectively enhances the cross-layer variation of images. Then, improved ECC is performed on the scrambled image where multiple elliptic curves and dynamic shared private keys can guarantee the forward secrecy of the image encryption algorithm. At last, the image is performed diffusion to obtain the final encrypted image. Simulation results and security analysis both indicate the image encryption algorithm has better performances in terms of key space, Shannon entropy, clipping attack resistance, etc.

Deep-Ocular: Improved Transfer Learning Architecture Using Self-Attention and Dense Layers for Recognition of Ocular Diseases.

It is difficult for clinicians or less-experienced ophthalmologists to detect early eye-related diseases. By hand, eye disease diagnosis is labor-intensive, prone to mistakes, and challenging because of the variety of ocular diseases such as glaucoma (GA), diabetic retinopathy (DR), cataract (CT), and normal eye-related diseases (NL). An automated ocular disease detection system with computer-aided diagnosis (CAD) tools is required to recognize eye-related diseases. Nowadays, deep learning (DL) algorithms enhance the classification results of retinograph images. To address these issues, we developed an intelligent detection system based on retinal fundus images. To create this system, we used ODIR and RFMiD datasets, which included various retinographics of distinct classes of the fundus, using cutting-edge image classification algorithms like ensemble-based transfer learning. In this paper, we suggest a three-step hybrid ensemble model that combines a classifier, a feature extractor, and a feature selector. The original image features are first extracted using a pre-trained AlexNet model with an enhanced structure. The improved AlexNet (iAlexNet) architecture with attention and dense layers offers enhanced feature extraction, task adaptability, interpretability, and potential accuracy benefits compared to other transfer learning architectures, making it particularly suited for tasks like retinograph classification. The extracted features are then selected using the ReliefF method, and then the most crucial elements are chosen to minimize the feature dimension. Finally, an XgBoost classifier offers classification outcomes based on the desired features. These classifications represent different ocular illnesses. We utilized data augmentation techniques to control class imbalance issues. The deep-ocular model, based mainly on the AlexNet-ReliefF-XgBoost model, achieves an accuracy of 95.13%. The results indicate the proposed ensemble model can assist dermatologists in making early decisions for the diagnosing and screening of eye-related diseases.

Adversarial Deep Learning based Dampster–Shafer data fusion model for intelligent transportation system

Intelligent Transportation Systems (ITS) have revolutionized transportation by incorporating advanced technologies for efficient and safe mobility. However, these systems face challenges ensuring security and resilience against adversarial attacks. This research addresses these challenges and introduces a novel Dampster–Shafer data fusion-based Adversarial Deep Learning (DS-ADL) Model for ITS in fog cloud environments. Our proposed model focuses on three levels of adversarial attacks: original image level, feature level, and decision level. Adversarial examples are generated at each level to evaluate the system’s vulnerability comprehensively. To enhance the system’s capabilities, we leverage the power of several vital components. Firstly, we employ Dempster–Shafer-based Multimodal Sensor Fusion, enabling the fusion of information from multiple sensors for improved scene understanding. This fusion approach enhances the system’s perception and decision-making abilities. For feature extraction and classification, we utilize ResNet 101, a deep learning architecture known for its effectiveness in computer vision tasks. We introduced a novel Monomodal Multidimensional Gaussian Model (MMGM-DD) based Adversarial Detection approach to detect adversarial examples. This detection mechanism enhances the system’s ability to identify and mitigate adversarial attacks in real-time. Additionally, we incorporate the Defensive Distillation method for adversarial training, which trains the model to be robust against attacks by exposing it to adversarial examples during the training process. To evaluate the performance of our proposed model, we utilize two datasets: Google Speech Command version 0.01 and the German Traffic Sign Recognition Benchmark (GTSRB). Evaluation metrics include latency delay and computation time (fog–cloud), accuracy, MSE, loss, and F-score for attack detection and defense. The results and discussions demonstrate the effectiveness of our Dampster–Shafer data fusion-based Adversarial Deep Learning Model in enhancing the robustness and security of ITS in fog–cloud environments. The model’s ability to detect and defend against adversarial attacks while maintaining low-latency fog–cloud operations highlights its potential for real-world deployment in ITS.

Feasibility of Using Pseudo-CT for Dosimetry, Radiomics, and Efficacy Assessment in IMRT/VMAT of Brain Tumors: A Multi-Omics Analysis

Pseudo-CT generated by convolutional neural networks (CNN) and planning MRI has facilitated the promotion of MRI-Only. The technology not only reduces the time and money spent on CT scans, but also eliminates the cumbersome CT-MR registration. The feasibility in Stereotactic Brain Radiotherapy has been analyzed in previous studies by our team. However, when the prescribed requirements are not met, IMRT/VMAT are still selected. The study aims to evaluate the feasibility of pseudo-CT in IMRT/VMAT for brain cancer via the following 5 aspects: (1) image difference, (2) dose accuracy, (3) radiomics feature, (4) efficacy assessment, and (5) correlation analysis. Brain tumor patients who had received radiotherapy at our institution and had planning MRI and CT were included in the study. Redesign of IMRT and VMAT radiotherapy plans according to 3 × 15Gy for each patient. Hounsfield unit (HU) values for PTV and OARs were used to assess image differences. And dose accuracy analysis contained a 2D dose volume histogram (DVH) metrics (Dmax, Dmean, D2%, D50%, D98%, HI, CI) and 3D gamma metrics (criteria: 1-3%/2mm, 1%/1mm, 10% threshold). Then 107 original image features of PTV and OARs were extracted for radiometry analysis. And tumor control probability (TCP) of PTV (Poisson model) and normal tissue complication probability (NTCP) of OARs (Lyman-Kutcher-Burman model) were used for the variance analysis of efficacy assessment. Wilcox-test was used for significance of differences test (0.05), and spearman correlation analysis was used to explore the key features of the dose bias. A total of 42 patients were included, with 42 planning CTs and pseudo-CTs (mDixon-T1), and 38 pseudo-CTs (mDixon-T1-CE). The median volume of PTV was 4.1 cc (range 0.5-27.3), with no significant differences in HU, DVH, 3D gamma, and NTCP/TCP metrics. The median local gamma passing rates (1%/1mm) between planning CTs and pseudo-CTs (mDixon-T1) were 93.1% (range 65.5%-99.7%, IMRT) and 93.3% (range 63.9%-99.6%, VMAT). And more than 85% original radiomics features have significant difference. Further, the feature HU-Min was found to be more correlated with dose metrics in the correlation analysis. We speculate that it may be caused by the smoothing of the low frequency signal before outputting image. And since Shape_MeshVolume, Shape_VoxelVolume and PTV volume difference are highly correlated with dose deviation, it indicates that dose deviation affected by CT-MR registration. This study has the potential to provide guidance for the clinical application of pseudo-CT in the MRI-Only workflow with IMRT/VMAT for brain tumors. These quantitative results strongly indicate pseudo-CT can be used as a substitute for the initial CT in IMRT/VMAT for small brain lesions (size <5 cm, numbers <5), but not for radiomics analysis. Additionally, the impact of inter-image differences on dose accuracy is less significant compared to the deviation caused by image registration.