Current Convolutional Neural Networks Research Articles

Convolution-transformer blend pyramid network for underwater image enhancement

Underwater images suffer from different types of degradation, where color degradation occurs in the spatial domain and edge degradation in the frequency domain. The high-quality underwater image enhancement represents a crucial milestone in advancing computer vision systems tailored for marine environments. This foundational endeavor encompasses a wide array of applications in computer vision tasks, including underwater inspection, underwater archaeology, and environmental monitoring. However, current convolutional neural network (CNN)-based pyramid frameworks primarily focus on capturing local features, often overlooking the significance of global semantic features that play a crucial role in understanding underwater scenes. Moreover, these frameworks handle spatial and frequency features independently, failing to enhance images by exploring correlation among domain-specific attributes for enabling information consistency. Besides, optimizing the model using a loss function with the same domain attributes from the ground truth may not lead to a better generalization ability. To solve these problems, we propose a new Convolution-Transformer Blend Pyramid Network (CTPN), which consists of a spatial branch and several frequency branches. The CTPN has four key components: a Swin transformer encoder, a CNN-Transformer aggregated encoder–decoder (CTED) and a blend pyramid framework. The Swin transformer encoder is employed to capture global semantic features, benefiting from its ability to extract long-range and global dependencies among features. The CTED fuses local features captured by CNN layers and global semantic features captured by the Swin transformer encoder in the spatial branch, with the help of the Cross-Model Fusion Module (CFM) and Skip-Aggregation Module (SAM). Subsequently, a blend pyramid framework is designed which not only progressively expands the transformed information of the previous domain branch to the current domain branch via the CTED-based refining operation, but also utilizes the proposed Domain Affinity Block (DAB) to explore the connection between domain attributes, ensuring information consistency. The experimental results demonstrate that the proposed method outperforms existing underwater image enhancement methods quantitatively and qualitatively.

Contextual embedding learning to enhance 2D networks for volumetric image segmentation

The segmentation of organs in volumetric medical images plays an important role in computer-aided diagnosis and treatment/surgery planning. Conventional 2D convolutional neural networks (CNNs) can hardly exploit the spatial correlation of volumetric data. Current 3D CNNs have the advantage to extract more powerful volumetric representations but they usually suffer from occupying excessive memory and computation nevertheless. In this study we aim to enhance the 2D networks with contextual information for better volumetric image segmentation. Accordingly, we propose a contextual embedding learning approach to facilitate 2D CNNs capturing spatial information properly. Our approach leverages the learned embedding and the slice-wisely neighboring matching as a soft cue to guide the network. In such a way, the contextual information can be transfered slice-by-slice thus boosting the volumetric representation of the network. Experiments on challenging prostate MRI dataset (PROMISE12) and abdominal CT dataset (CHAOS) show that our contextual embedding learning can effectively leverage the inter-slice context and improve segmentation performance. The proposed approach is a plug-and-play, and memory-efficient solution to enhance the 2D networks for volumetric segmentation. Our code is publicly available at https://github.com/JuliusWang-7/CE_Block.

Making the unmodulated Pyramid wavefront sensor smart

Almost all current and future high-contrast imaging instruments will use a Pyramid wavefront sensor (PWFS) as a primary or secondary wavefront sensor. The main issue with the PWFS is its nonlinear response to large phase aberrations, especially under strong atmospheric turbulence. Most instruments try to increase its linearity range by using dynamic modulation, but this leads to decreased sensitivity, most prominently for low-order modes, and makes it blind to petal-piston modes. In the push toward high-contrast imaging of fainter stars and deeper contrasts, there is a strong interest in using the PWFS in its unmodulated form. Here, we present closed-loop lab results of a nonlinear reconstructor for the unmodulated PWFS of the Magellan Adaptive Optics extreme (MagAO-X) system based on convolutional neural networks (CNNs). We show that our nonlinear reconstructor has a dynamic range of >600 nm root-mean-square (RMS), significantly outperforming the linear reconstructor that only has a 50 nm RMS dynamic range. The reconstructor behaves well in closed loop and can obtain >80% Strehl at 875 nm under a large variety of conditions and reaches higher Strehl ratios than the linear reconstructor under all simulated conditions. The CNN reconstructor also achieves the theoretical sensitivity limit of a PWFS, showing that it does not lose its sensitivity in exchange for dynamic range. The current CNN’s computational time is 690 µs, which enables loop speeds of >1 kHz. On-sky tests are foreseen soon and will be important for pushing future high-contrast imaging instruments toward their limits.

SC-Track: a robust cell-tracking algorithm for generating accurate single-cell lineages from diverse cell segmentations.

Computational analysis of fluorescent timelapse microscopy images at the single-cell level is a powerful approach to study cellular changes that dictate important cell fate decisions. Core to this approach is the need to generate reliable cell segmentations and classifications necessary for accurate quantitative analysis. Deep learning-based convolutional neural networks (CNNs) have emerged as a promising solution to these challenges. However, current CNNs are prone to produce noisy cell segmentations and classifications, which is a significant barrier to constructing accurate single-cell lineages. To address this, we developed a novel algorithm called Single Cell Track (SC-Track), which employs a hierarchical probabilistic cache cascade model based on biological observations of cell division and movement dynamics. Our results show that SC-Track performs better than a panel of publicly available cell trackers on a diverse set of cell segmentation types. This cell-tracking performance was achieved without any parameter adjustments, making SC-Track an excellent generalized algorithm that can maintain robust cell-tracking performance in varying cell segmentation qualities, cell morphological appearances and imaging conditions. Furthermore, SC-Track is equipped with a cell class correction function to improve the accuracy of cell classifications in multiclass cell segmentation time series. These features together make SC-Track a robust cell-tracking algorithm that works well with noisy cell instance segmentation and classification predictions from CNNs to generate accurate single-cell lineages and classifications.

Using transfer learning-based plant disease classification and detection for sustainable agriculture

Subsistence farmers and global food security depend on sufficient food production, which aligns with the UN's “Zero Hunger,” “Climate Action,” and “Responsible Consumption and Production” sustainable development goals. In addition to already available methods for early disease detection and classification facing overfitting and fine feature extraction complexities during the training process, how early signs of green attacks can be identified or classified remains uncertain. Most pests and disease symptoms are seen in plant leaves and fruits, yet their diagnosis by experts in the laboratory is expensive, tedious, labor-intensive, and time-consuming. Notably, how plant pests and diseases can be appropriately detected and timely prevented is a hotspot paradigm in smart, sustainable agriculture remains unknown. In recent years, deep transfer learning has demonstrated tremendous advances in the recognition accuracy of object detection and image classification systems since these frameworks utilize previously acquired knowledge to solve similar problems more effectively and quickly. Therefore, in this research, we introduce two plant disease detection (PDDNet) models of early fusion (AE) and the lead voting ensemble (LVE) integrated with nine pre-trained convolutional neural networks (CNNs) and fine-tuned by deep feature extraction for efficient plant disease identification and classification. The experiments were carried out on 15 classes of the popular PlantVillage dataset, which has 54,305 image samples of different plant disease species in 38 categories. Hyperparameter fine-tuning was done with popular pre-trained models, including DenseNet201, ResNet101, ResNet50, GoogleNet, AlexNet, ResNet18, EfficientNetB7, NASNetMobile, and ConvNeXtSmall. We test these CNNs on the stated plant disease detection and classification problem, both independently and as part of an ensemble. In the final phase, a logistic regression (LR) classifier is utilized to determine the performance of various CNN model combinations. A comparative analysis was also performed on classifiers, deep learning, the proposed model, and similar state-of-the-art studies. The experiments demonstrated that PDDNet-AE and PDDNet-LVE achieved 96.74% and 97.79%, respectively, compared to current CNNs when tested on several plant diseases, depicting its exceptional robustness and generalization capabilities and mitigating current concerns in plant disease detection and classification.

Nearshore Bathymetry from ICESat-2 LiDAR and Sentinel-2 Imagery Datasets Using Physics-Informed CNN

The recently developed Ice, Cloud, and Land Elevation Satellite 2 (ICESat-2), furnished with the Advanced Terrain Laser Altimeter System (ATLAS), delivers considerable benefits in providing accurate bathymetric data across extensive geographical regions. By integrating active lidar-derived reference seawater depth data with passive optical remote sensing imagery, efficient bathymetry mapping is facilitated. In recent times, machine learning models are frequently used to define the nonlinear connection between remote sensing spectral data and water depths, which consequently results in the creation of bathymetric maps. A salient model among these is the convolutional neural network (CNN), which effectively integrates contextual information concerning bathymetric points. However, current CNN models and other machine learning approaches mainly concentrate on recognizing mathematical relationships within the data to determine a water depth function and remote sensing spectral data, while oftentimes disregarding the physical light propagation process in seawater before reaching the seafloor. This study presents a physics-informed CNN (PI-CNN) model which incorporates radiative transfer-based data into the CNN structure. By including the shallow water double-band radiative transfer physical term (swdrtt), this model enhances seawater spectral features and also considers the context surroundings of bathymetric pixels. The effectiveness and reliability of our proposed PI-CNN model are verified using in situ data from St. Croix and St. Thomas, validating its correctness in generating bathymetric maps with a broad experimental R2 accuracy exceeding 95% and remaining errors below 1.6 m. Preliminary results suggest that our PI-CNN model surpasses conventional methodologies.

MSE-Net: A novel master–slave encoding network for remote sensing scene classification

Remote sensing scene (RSS) image classification plays a vital role in various fields such as urban planning and environmental protection. However, due to higher inter-class similarity and intra-class variability, achieving accurate classification for RSS images poses a considerable challenge for current convolutional neural networks (CNNs)-based and visual transformer (ViT)-based methods. To address these issues, this paper proposes a novel dual-encoding method named master–slave encoding network (MSE-Net) from two perspectives of feature extraction and fusion. The master encoder, based on ViT, extracts higher-level semantic features, while the slave encoder, based on CNN, captures relative lower-level spatial structure information. Secondly, to integrate feature information from the two encoders effectively, this paper further develop two fusion strategies. The first strategy involves the auxiliary enhancement units (AEUs), which eliminates semantic divergence between the two encoders, enhances spatial context awareness of the slave encoder and promotes effective feature learning. The interactive perception unit (IPU), as the second strategy, facilitates interaction and integration of the two encoders’ representations to extract more discriminative feature information. In addition, we conducted comparative experiments on four widely-used RSS datasets, including RSSCN7, SIRI-WHU, the aerial image dataset (AID) and NWPU-RESISC45 (NWPU45), to verify the effectiveness of MSE-Net. The experimental results demonstrate that MSE-Net achieved state-of-the-art (SOTA) performance across all the datasets.

Hybrid Architecture Strategies for the Prediction of Acute Pulmonary Embolism from Computed Tomography Images

The timely identification of pulmonary embolism is of utmost importance, as the condition has the potential to be life-threatening if not promptly addressed. The assessment of the severity of a pulmonary embolism (PE) frequently necessitates a time-consuming and potentially life-threatening estimation by a medical practitioner. The primary objective of the study was to investigate the potential utility of an artificial neural network in assisting physicians with the identification and prediction of pulmonary embolism risk in patients. Deep learning algorithms are frequently employed in medical imaging to enhance image analysis due to their ability to automatically learn representations from large datasets, as opposed to relying on pre-programmed instructions . The implementation of automated systems has the potential to decrease the level of physical effort needed and enhance the efficiency of diagnostic procedures for medical professionals. Efficient training and calculation processes are crucial for the proper execution of the implementation. The Tensor Processing Units (TPUs) developed by Google are employed to expedite the process of training., with the computational tasks being executed through Google Colab, a platform offered by Google Cloud TPUs. In order to achieve outcomes comparable to human judgment, deep learning algorithms engage in reasonable assessments of data based on a predetermined logical framework. Diagnosing pulmonary embolism (PE), a potentially lethal yet curable condition, poses challenges in early detection. A distinctive convolutional neural network (CNN) model was developed and examined for the purpose of distinguishing between pulmonary embolism (PE) and computed tomography (CT) pictures. The proposed study yields a precision rate of 91.2%, showcasing an enhancement compared to current convolutional neural network (CNN) architectures that include limited trainable parameters. Furthermore, our model provides interpretability by the utilization of computed tomography (CT) images, specifically in the inferno and bone models. Our proposed deep learning model has the potential to predict the presence of PE and other associated features in current cases.

Enhancing Breast Cancer Detection through Thermal Imaging and Customized 2D CNN Classifiers

Breast cancer is one of the most prevalent and life-threatening forms of cancer due to its aggressive nature and high mortality rates. Early detection significantly improves a patient's chances of survival. Currently, mammography is the preferred diagnostic method, but it has drawbacks such as radiation exposure and high costs. In response to these challenges, thermography has become a less invasive and cost-effective alternative, gaining popularity. We aim to develop a cutting-edge model for breast cancer detection based on thermal imaging. The initial phase involves creating a customized machine-learning (ML) model built on convolutional neural networks (CNN). Subsequently, this model undergoes training using a diverse dataset of thermal images depicting breast abnormalities, enabling it to identify breast cancer effectively. This innovative approach promises to revolutionize breast cancer diagnosis and offers a safer and more accessible alternative to traditional methods. In our recent study, we leveraged thermal image processing techniques to forecast breast cancer precisely based on its external manifestations, particularly in cases where multiple factors are interconnected. This research employed various image classification methods to categorize breast cancer effectively. Our comprehensive approach encompassed segmentation, texture-based feature extraction from thermal images, and subsequent image classification, leading to the successful detection of malignant images. Our study harnessed the power of machine learning to create a tailored classifier, merging key components from GoogleNet, including the utilization of 2D CNNs and activation functions, with the ResNet architecture. This hybrid approach incorporated batch normalization layers following each convolutional layer and employed max-pooling to enhance classification accuracy. Next, we used a sample dataset of carefully selected images from DMR-IR to train our proposed model. The outcomes of this training demonstrated significant improvement over existing methods, with our suggested 2D CNN classifiers achieving an impressive classification rate of 95%, surpassing both the SVM and current CNN models, which achieved rates of 91% and 71%, respectively.

Advancing medical data classification through federated learning and blockchain incentive mechanism: implications for modern software systems and applications

The key issue of medical data is patient information sensitivity and dataset finiteness, which need to guarantee high-efficient training. Besides, the current convolutional neural network has a low image classification and poor robustness concerning antagonistic samples. A lack of scalability in healthcare federated learning and incentive mechanism hinders the attraction of ample high-quality datasets. This paper proposes a Federated Learning Incentive Mechanism for Medical Data Classification (FedIn-MC). It realizes a collaborative model training of multi-party medical institutions through the combination of federated learning and blockchain. There is a marked improvement to the model’s robustness through a combination of the distance loss function and the prototype loss regulation. In addition, this incentive mechanism of blockchain in the project is applied to calculate client contribution values and encourage healthcare institutions to active training model participation. Simulation results verify an accomplishment of a multi-party training. With regard to image classifications, this framework also has a higher classification accuracy and stronger robustness concerning invisible class samples.

Global routing between capsules

Current convolutional neural networks (CNNs) lack viewpoint equivariance. Hence, they perform poorly when dealing with viewpoints unseen during training procedures. CNN achieves invariance via pooling operations in image classification tasks. However, the pooling operation does not necessarily improve viewpoint generalization, rather relying on more data to achieve viewpoint equivariance. Capsule network (CapsNet) is proposed to tackle this issue, but it is inefficient and inaccurate when applied to complex datasets. We propose a novel CapsNet architecture called Global Routing CapsNet (GR-CapsNet) to solve this problem. Specifically, colored background in the input image can generate invalid background voting capsules to reduce the performance of CapsNet. Therefore, we first construct a dynamic linear unit (DLU), which avoids the generation of invalid background voting capsules. Then we present two extra learnable units: frequency domain unit (FDU) and spatial unit (SPU). The former is used to capture finer features in the frequency domain and aims to improve classification performance on complex datasets. The latter is applied to construct the spatial relationship between the voting capsules and component capsules and aims to enhance robustness to affine transformation. Finally, we propose a global routing mechanism to simplify the routing process for CapsNet, which obtains more feature information to improve the performance of CapsNet. Extensive experiments on nine datasets show that our method obtains better robustness and generalization and achieves SOTA performance compared to other related methods. And it has fewer the number of parameters and GPU memory consumption than these related methods. The source code is available on https://github.com/cwpl/GR-CapsNet.

Cross-device target migration intelligent diagnosis method of wind power gearbox faults

The current convolutional neural network (CNN) deep learning algorithm for wind power gearbox fault diagnosis requires a substantial amount of high-quality tagged data from testbeds to achieve accurate results. However, it may not perform well in practical engineering scenarios where there is limited fault tag data and incomplete fault information. To overcome this challenge, a novel approach utilizing cross-device target transfer for intelligent diagnosis of wind turbine gearbox faults is proposed. This method transforms the fault diagnosis of wind turbine gearboxes into an image recognition and classification task. Initially, the source domain gearbox testbed data is subjected to dimension transformation using the Gramian angular difference field technique, resulting in the generation of small sample two-dimensional images. This transformation simulates the limited fault tag data scenario encountered in practical engineering. Once the fault features have been augmented, the small sample source domain gearbox testbed data is fed into the IConvNeXt deep CNNs. These CNNs are interconnected and specifically designed for the field. They aim to extract and recognize fault features, thereby creating a pre-training model. The pre-training model is then transferred to the target domain wind power gearbox data for fault diagnosis experiments. The results of these experiments demonstrate the significant effectiveness of the proposed method in recognizing faults in small sample wind power gearboxes within the target domain. Overall, this method successfully achieves the goal of intelligent fault diagnosis through target transfer. It addresses the challenge of limited fault tag data and incomplete fault information, thereby enhancing the performance of CNNs in practical engineering scenarios.

Viewing Channel as Sequence Rather Than Image: A 2-D Seq2Seq Approach for Efficient MIMO-OFDM CSI Feedback

In this paper, we aim to design an effective learning-based channel state information (CSI) feedback scheme for the multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems from a physics-inspired perspective. We first argue that the CSI matrix of a MIMO-OFDM system is physically closer to a two-dimensional (2-D) sequence rather than an image due to its apparent unsmoothness, non-scalability, and translational variance within both the spatial and frequency domains. On this basis, we introduce a 2-D long short-term memory (LSTM) neural network to represent the CSI and propose a 2-D sequence-to-sequence (Seq2Seq) model for CSI compression and reconstruction. Specifically, one two-layer 2-D LSTM is used for CSI feature extraction, and the other is used for CSI representation and reconstruction. The proposed scheme can not only fully utilize the unique 2-D characteristics of CSI but also preserve the index information and unsmooth features of the CSI matrix compared with current convolutional neural network (CNN) based schemes. We show that the computational complexity of the proposed scheme is linear in the number of transmit antennas and subcarriers. Its key performances, like reconstruction accuracy, convergence speed, generalization ability after short-term training, and robustness to lossy feedback, are comprehensively compared with existing popular convolutional networks. Experimental results show that our scheme can bring up to nearly 7 dB gain in reconstruction accuracy under the same overhead and reduce feedback overhead by up to 75% under the same accuracy compared with the conventional CNN-based approaches.

Beyond CNNs: Exploiting Further Inherent Symmetries in Medical Image Segmentation.

Automatic tumor or lesion segmentation is a crucial step in medical image analysis for computer-aided diagnosis. Although the existing methods based on convolutional neural networks (CNNs) have achieved the state-of-the-art performance, many challenges still remain in medical tumor segmentation. This is because, although the human visual system can detect symmetries in 2-D images effectively, regular CNNs can only exploit translation invariance, overlooking further inherent symmetries existing in medical images, such as rotations and reflections. To solve this problem, we propose a novel group equivariant segmentation framework by encoding those inherent symmetries for learning more precise representations. First, kernel-based equivariant operations are devised on each orientation, which allows it to effectively address the gaps of learning symmetries in existing approaches. Then, to keep segmentation networks globally equivariant, we design distinctive group layers with layer-wise symmetry constraints. Finally, based on our novel framework, extensive experiments conducted on real-world clinical data demonstrate that a group equivariant Res-UNet (called GER-UNet) outperforms its regular CNN-based counterpart and the state-of-the-art segmentation methods in the tasks of hepatic tumor segmentation, COVID-19 lung infection segmentation, and retinal vessel detection. More importantly, the newly built GER-UNet also shows potential in reducing the sample complexity and the redundancy of filters, upgrading current segmentation CNNs, and delineating organs on other medical imaging modalities.

S2Former: Parallel Spectral–Spatial Transformer for Hyperspectral Image Classification

Due to their excellent representation talent in local features, the convolutional neural network (CNN) has achieved favourable performance in hyperspectral image (HSI) classification tasks. Nevertheless, current CNN models exhibit a marked flaw: they are hard to model the dependencies in long-range distanced positions. This flaw becomes more problematic for the HSI classification task, which targets extracting more discriminative features in local and global dimensions from limited samples. In this paper, we introduce a spatial–spectral transformer (S2Former), which explores spatial and spectral feature extraction in a dual-stream framework for HSI Classification. S2Former, which consists of a spatial transformer and a spectral transformer in parallel branches, extracts the discriminative feature in spatial and spectral dimensions. More specifically, we propose multi-head spatial self-attention to capture the long-range spatial dependency of non-adjacent HSI pixels in a spatial transformer. In the spectral transformer, we propose multi-head covariance spectral attention to mine and represent spectral signatures by computing covariance-based channel maps. Meanwhile, the local activation feed-forward network is developed to complement local details. Extensive experiments conducted on four publicly available datasets indicate that our S2Former achieves state-of-the-art performance for the HSI classification task.

Artificial Intelligence's Use in the Diagnosis of Mouth Ulcers: A Systematic Review.

Artificial intelligence (AI) has been cited as being helpful in the diagnosis of diseases, the prediction of prognoses, and the development of patient-specific therapeutic strategies. AI can help dentists, in particular, when they need to make important judgments quickly. It can eliminate human mistakes in making decisions, resulting in superior and consistent medical treatment while lowering the workload on dentists.The existing studies relevant to the study and application of AI in the diagnosis of various forms of mouth ulcers are reviewed in this work. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) standards were followed in the preparation of the review. There were no rule violations, with the significant exception of the use of a better search method that led to more accurate findings. Using search terms mainly such as AI, oral health, oral ulcers, oral herpes simplex, oral lichen planus, pemphigus vulgaris, recurrent aphthous ulcer (RAU), oral cancer, premalignant and malignant disorders, etc., a comprehensive search was carried out in the reliable sources of literature, namely PubMed, Scopus, Embase, Web of Science, Ovid, Global Health, and PsycINFO. For all papers, exhaustive searches were done using inclusion criteria as well as exclusion criteria between June 28, 2018, and June 28, 2023. An AI framework for the automatic categorization of oral ulcers from oral clinical photographs was developed by the authors, and it performed satisfactorily. The newly designed AI model works better than the current convolutional neural network image categorization techniques and shows a fair level of precision in the classification of oral ulcers. However, despite being useful for identifying oral ulcers, the suggested technique needs a broader set of data for validation and training purposes before being used in clinical settings. Automated OCSCC identification using a deep learning-based technique is a quick, harmless, affordable, and practical approach to evaluating the effectiveness of cancer treatment. The categorization and identification of RAU lesions through the use of non-intrusive oral pictures using the previously developed ResNet50 and YOLOV algorithms demonstrated better accuracy as well as adequate potential for the future, which could be helpful in clinical practice. Moreover, the most reliable projections for the likelihood of the presence or absence of RAU were made by the optimized neural network. The authors also discovered variables associated with RAU that might be used as input information to build artificial neural networks that anticipate RAU.

Towards an adversarially robust convolutional neural network for automated diagnosis of malaria infection from microscopy images

Malaria is a life-threatening mosquito-borne disease of global importance, and it is most prevalent in the low-income countries of the developing world. The diagnosis of malaria infection is conventionally performed by laboratorians through visual examination of blood smear images under a microscope. However, the results of such diagnostic testing depend on the laboratory technicians' experience with staining and image interpretation, which can be severely lacking in low-resource settings. Convolutional neural networks (CNNs) have great potential for automated malaria diagnosis from images without the need of human expert interaction. However, studies have shown that current CNNs are not robust to adversarial noises which are small input perturbations that cause CNNs to incorrectly classify medical images. In this study, we developed a CNN for malaria parasite identification and evaluated it on a dataset of 27,558 cell images through patient-level cross validation. To improve robustness against adversarial noises for this application, the structure of the CNN was specially designed to limit the receptive field of the output, and adversarial training was applied with gradually increasing level of noises. Compared to three baseline networks (ResNet-18, MobileNet, and MnasNet) and two variants of the CNN, our custom-designed CNN achieved a substantially lower computation cost and better adversarial robustness against PGD-generated adversarial noises on the malaria image dataset. Our study holds the promise of developing a robust and affordable solution for automated diagnosis of malarial infection.

Convolutional neural networks for vision neuroscience: significance, developments, and outstanding issues.

Convolutional Neural Networks (CNN) are a class of machine learning models predominately used in computer vision tasks and can achieve human-like performance through learning from experience. Their striking similarities to the structural and functional principles of the primate visual system allow for comparisons between these artificial networks and their biological counterparts, enabling exploration of how visual functions and neural representations may emerge in the real brain from a limited set of computational principles. After considering the basic features of CNNs, we discuss the opportunities and challenges of endorsing CNNs as in silico models of the primate visual system. Specifically, we highlight several emerging notions about the anatomical and physiological properties of the visual system that still need to be systematically integrated into current CNN models. These tenets include the implementation of parallel processing pathways from the early stages of retinal input and the reconsideration of several assumptions concerning the serial progression of information flow. We suggest design choices and architectural constraints that could facilitate a closer alignment with biology provide causal evidence of the predictive link between the artificial and biological visual systems. Adopting this principled perspective could potentially lead to new research questions and applications of CNNs beyond modeling object recognition.

Two-Stream Boundary-Aware Neural Network for Concrete Crack Segmentation and Quantification

Cracks can be important performance indicators for determining damage processes in new and existing concrete structures. In recent years, deep convolutional neural networks (CNNs) have shown great potential in automatic crack detection and segmentation. However, most of the current CNNs tend to lose high-resolution details and, therefore, lead to blurry object boundaries; this results in poor performance for crack images with complex backgrounds in engineering structures. This study proposes a two-stream boundary-aware crack segmentation (BACS) network that combines semantic image segmentation with semantically informed edge detection explicitly. Firstly, a high-resolution network (HRNet) is utilized in the segmentation branch for strong high-resolution representations through repeatedly conducting multi-scale fusions across parallel convolutions. Furthermore, an edge branch is utilized for preserving fine-grained details of elongated thin cracks, which adopts a modified dynamic feature fusion (DFF) network to produce more accurate and sharper edge predictions. The proposed method is evaluated using a dataset of 1,892 images for three different scenarios. The results show that the mean intersection-over-union (mIoU) scores reach 79.26%, 68.74%, and 70.31% for pure crack, complex background, and variable-width scenarios, respectively. In addition, crack width quantification is performed to validate the accuracy in terms of engineering practice. The BACS achieves high accuracy with an average absolute error of 0.0992 mm, which corresponds to approximately two pixels in the images. In conclusion, the study provides an effective solution for the crack segmentation task, especially for the variable-width scenario, providing an accurate data foundation for the digital twin of concrete structures.

Proposing a new approach based on convolutional neural networks and random forest for the diagnosis of Parkinson's disease from speech signals

Parkinson which occurs because of affected motor system by central nervous system is a neurodegenerative disease which is often seen in community. This disease, which is frequently seen especially in the elderly, brings problems such as speech disorders in patients. It is seen that with the rapidly developing deep learning and machine learning methods in recent years, it is possible to distinguish speech disorders in PD patients at a high rate and quickly. In this study, PD diagnosis was performed using datasets containing voice signals of healthy individuals and PD patients (PD_Dataset and PDO_Dataset). Current convolutional neural networks (CNN) and machine learning (ML) algorithms for PD diagnosis have been examined and a comparative performance analysis has been made. In addition, a different method called SkipConNet + RF based on CNN and random forest (RF) has been proposed for PD diagnosis. With the proposed SkipConNet, important features were obtained from the speech signals; then, the estimation process was performed using the RF algorithm. The proposed method provided an improvement between 3% and 17.19% in the performance of RF algorithms. In addition, the SkipConNet + RF method showed the highest success with 99.11% accuracy in the PD_Dataset dataset and 98.30% in the PDO_Dataset dataset.