Inter-class Similarity Research Articles

EAGNet: A method for automatic extraction of agricultural greenhouses from high spatial resolution remote sensing images based on hybrid multi-attention

The timely and accurate acquisition of greenhouse information is crucial for strategically planning modern agriculture. However, existing methods are affected by the close spacing between agricultural greenhouses, intra-class diversity, and inter-class similarity, resulting in missed and incorrect extraction phenomena. Here, we propose a model for agricultural greenhouse extraction (i.e., EAGNet), which includes a residual block improvement module (RBIM) and boundary segmentation module (BSM) that solve the problem of densely distributed agricultural greenhouse-boundary adhesion. We constructed a class attention module (CAM) to address the leakage extraction phenomenon in agricultural greenhouses caused by intra-class diversity and introduced an object contextual representation module (OCRM) to address the incorrect extraction of agricultural greenhouses caused by the similarity between classes. Experiments on a self-made agricultural greenhouse dataset showed that EAGNet achieved the best extraction results among all compared methods.

Deep attentive convolutional neural network for automatic grading of imbalanced diabetic retinopathy in retinal fundus images.

Automated fine-grained diabetic retinopathy (DR) grading was of great significance for assisting ophthalmologists in monitoring DR and designing tailored treatments for patients. Nevertheless, it is a challenging task as a result of high intra-class variations, high inter-class similarities, small lesions, and imbalanced data distributions. The pivotal factor for the success in fine-grained DR grading is to discern more subtle associated lesion features, such as microaneurysms (MA), Hemorrhages (HM), soft exudates (SE), and hard exudates (HE). In this paper, we constructed a simple yet effective deep attentive convolutional neural network (DACNN) for DR grading and lesion discovery with only image-wise supervision. Designed as a top-down architecture, our model incorporated stochastic atrous spatial pyramid pooling (sASPP), global attention mechanism (GAM), category attention mechanism (CAM), and learnable connected module (LCM) to better extract lesion-related features and maximize the DR grading performance. To be concrete, we devised sASPP combining randomness with atrous spatial pyramid pooling (ASPP) to accommodate the various scales of the lesions and struggle against the co-adaptation of multiple atrous convolutions. Then, GAM was introduced to extract class-agnostic global attention feature details, whilst CAM was explored for seeking class-specific distinctive region-level lesion feature information and regarding each DR severity grade in an equal way, which tackled the problem of imbalance DR data distributions. Further, the LCM was designed to automatically and adaptively search the optimal connections among layers for better extracting detailed small lesion feature representations. The proposed approach obtained high accuracy of 88.0% and kappa score of 88.6% for multi-class DR grading task on the EyePACS dataset, respectively, while 98.5% AUC, 93.8% accuracy, 87.9% kappa, 90.7% recall, 94.6% precision, and 92.6% F1-score for referral and non-referral classification on the Messidor dataset. Extensive experimental results on three challenging benchmarks demonstrated that the proposed approach achieved competitive performance in DR grading and lesion discovery using retinal fundus images compared with existing cutting-edge methods, and had good generalization capacity for unseen DR datasets. These promising results highlighted its potential as an efficient and reliable tool to assist ophthalmologists in large-scale DR screening.

Deep Residual Vector Encoding for Vein Recognition

Vein recognition has been drawing more attention recently because it is highly secure and reliable for practical biometric applications. However, underlying issues such as uneven illumination, low contrast, and sparse patterns with high inter-class similarities make the traditional vein recognition systems based on hand-engineered features unreliable. Recent successes of convolutional neural networks (CNNs) for large-scale image recognition tasks motivate us to replace the traditional hand-engineered features with the superior CNN to design a robust and discriminative vein recognition system. To address the difficulty of direct training or fine-tuning of a CNN with existing small-scale vein databases, a new knowledge transfer approach is formulated using pre-trained CNN models together with a training dataset (e.g., ImageNet) as a robust descriptor generation machine. With the generated deep residual descriptors, a very discriminative model, namely deep residual vector encoding (DRVE), is proposed by a hierarchical design of dictionary learning, coding, and classifier training procedures. Rigorous experiments are conducted with a high-quality hand-dorsa vein database, and superior recognition results compared with state-of-the-art models fully demonstrate the effectiveness of the proposed models. An additional experiment with the PolyU multispectral palmprint database is designed to illustrate the generalization ability.

Feature enhancement network for CNV typing in optical coherence tomography images

Objective. Choroidal neovascularization (CNV) is a characteristic feature of wet age-related macular degeneration, which is one of the main causes of blindness in the elderly. Automatic classification of CNV in optical coherence tomography images plays an auxiliary role in the clinical treatment of CNV. Approach. This study proposes a feature enhancement network (FE-net) to discriminate between different CNV types with high inter-class similarity. The FE-net consists of two branches: discriminative FE and diverse FE. In the discriminative FE branch, a novel class-specific feature extraction module is introduced to learn class-specific features, and the discriminative loss is introduced to make the learned features more discriminative. In the diverse FE branch, the attention region selection is used to mine the multi-attention features from feature maps in the same class, and the diverse loss is introduced to guarantee that the attention features are different, which can improve the diversity of the learned features. Main results. Experiments were conducted on our CNV dataset, with significant accuracy of 92.33%, 87.45%, 90.10%, and 91.25% on ACC, AUC, SEN, and SPE, respectively. Significance. These results demonstrate that the proposed method can effectively learn the discriminative and diverse features to discriminate subtle differences between different types of CNV. And accurate classification of CNV plays an auxiliary role in clinical treatmen.

A Triple-Structure Network Model Based upon MobileNet V1 and Multi-Loss Function for Facial Expression Recognition

Existing facial expression recognition methods have some drawbacks. For example, it becomes difficult for network learning on cross-dataset facial expressions, multi-region learning on an image did not extract the overall image information, and a frequency multiplication network did not take into account the inter-class and intra-class features in image classification. In order to deal with the above problems, in our current research, we raise a symmetric mode to extract the inter-class features and intra-class diversity features, and then propose a triple-structure network model based upon MobileNet V1, which is trained via a new multi-branch loss function. Such a proposed network consists of triple structures, viz., a global branch network, an attention mechanism branch network, and a diversified feature learning branch network. To begin with, the global branch network is used to extract the global features of the facial expression images. Furthermore, an attention mechanism branch network concentrates to extract inter-class features. In addition, the diversified feature learning branch network is utilized to extract intra-class diverse features. The network training is performed by using multiple loss functions to decrease intra-class differences and inter-class similarities. Finally, through ablation experiments and visualization, the intrinsic mechanism of our triple-structure network model is proved to be very reasonable. Experiments on the KDEF, MMI, and CK+ datasets show that the accuracy of facial expression recognition using the proposed model is 1.224%, 13.051%, and 3.085% higher than that using MC-loss (VGG16), respectively. In addition, related comparison tests and analyses proved that our raised triple-structure network model reaches better performance than dozens of state-of-the-art methods.

Attributes Guided Feature Learning for Vehicle Re-Identification

Vehicle Re-ID has recently attracted enthusiastic attention due to its potential applications in smart city and urban surveillance. However, it suffers from large intra-class variation caused by view variations and illumination changes, and inter-class similarity especially for different identities with a similar appearance. To handle these issues, in this paper, we propose a novel deep network architecture, which guided by meaningful attributes including camera views, vehicle types and colors for vehicle Re-ID. In particular, our network is end-to-end trained and contains three subnetworks of deep features embedded by the corresponding attributes. For network training, we annotate the view labels on the VeRi-776 dataset. Note that one can directly adopt the pre-trained view (as well as type and color) subnetwork on the other datasets with only ID information, which demonstrates the generalization of our model. Extensive experiments on the benchmark datasets VeRi-776 and VehicleID suggest that the proposed approach achieves the promising performance and yields to a new state-of-the-art for vehicle Re-ID.

Exploring Self-Attention Graph Pooling With EEG-Based Topological Structure and Soft Label for Depression Detection

Electroencephalogram (EEG) has been widely used in neurological disease detection, i.e., major depressive disorder (MDD). Recently, some deep EEG-based MDD detection attempts have been proposed and achieved promising performance. These works, however, still suffer from the following limitations, such as insufficient exploration of the EEG-based topological structure, information loss caused by high-dimensional data compression, and under-estimation of intra-class difference and inter-class similarity. To solve these issues, we propose an EEG-based MDD detection model named <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</u> elf-attention <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">G</u> raph <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">P</u> ooling with <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</u> oft <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</u> abel (SGP-SL). Specifically, we explore the local and global connections among EEG channels to construct an EEG-based graph in advance. By leveraging multiple self-attention graph pooling modules, the constructed graph is then gradually refined, followed by graph pooling, to aggregate information from less-important nodes to more-important ones. In this way, the feature representation with better discriminability can be learned from EEG signals. In addition, the soft label strategy is also adopted to build the loss function, aiming to further enhance the feature discriminability. Experimental results on the MODMA dataset demonstrate the superiority of the proposed method. What's more, extensive ablation studies are conducted to verify the effectiveness of the proposed elements in our model.

Prior Knowledge-Aware Fusion Network for Prediction of Macrovascular Invasion in Hepatocellular Carcinoma.

Macrovascular invasion (MaVI) is a major threat to survival in hepatocellular carcinoma (HCC), which should be treated as early as possible to ensure safety and efficacy. In this aspect, MaVI prediction can be helpful. However, MaVI prediction is difficult because of the inter-class similarity and intra-class variation of HCC in computed tomography (CT) images. Moreover, existing methods fail to include clinical priori knowledge associated with HCC, leading to incomprehensive information extraction. In this paper, we proposed a prior knowledge-aware fusion network (PKAFnet) to accurately achieve MaVI prediction in CT images. First, a perception module was presented to extract features related to tumor marginal heterogeneity in the graph domain, which contributed to rotation invariance and captured intensity variations of tumor margin. Second, a tumor segmentation network was built to obtain global information of a 3D tumor image and information associated with tumor internal heterogeneity in the image domain. Finally, multi-domain features associated with the tumor margin and tumor region were combined by using a multi-domain attentional feature fusion module. Thus, by incorporating MaVI-related prior knowledge, our PKAFnet can alleviate overfitting, which can improve the discriminative ability. The proposed PKAFnet was validated on a multi-center dataset, and remarkable performance was achieved in an independent testing set. Moreover, the interpretability of perception module and segmentation network were presented in our paper, which illustrated the effectiveness and credibility of PKAFnet. Therefore, the proposed method showed great application potential for MaVI prediction.

V2AnomalyVec: Deep Discriminative Embeddings for Detecting Anomalous Activities in Surveillance Videos

Understanding patterns in surveillance videos is always challenging due to the rapid movement of the crowd, occlusions, cluttered backgrounds, intraclass variations, and interclass similarities existing among normal and abnormal event classes. In the case of autoencoder-based normality model trained using segments of normal events only, there may be chances of anomalous events to be reconstructed well, leading to anomalous events detected as normal events. In this work, we propose deep discriminative embedding (DDE)-based framework using residual spatiotemporal auotencoders (R-STAEs) that learn V2AnomalyVec embeddings. In place of R-STAEs, any promising deep architectures to extract deep features from video segments of normal and abnormal events can also be used to form V2AnomalyVec. Results on four benchmark datasets demonstrate that the V2AnomalyVec-based approach performs significantly better than the normality model-based approaches and other state-of-the-art approaches. Studies with a reduced number of abnormal video segments to train the model prove that the proposed approach is also appropriate for detecting anomalous events even with less amount of abnormal training data.

Skin lesion classification using CNNs with grouping of multi-scale attention and class-specific loss weighting

As one of the most common cancers globally, the incidence of skin cancer has been rising. Dermoscopy-based classification has become the most effective method for the diagnosis of skin lesion types due to its accuracy and non-invasive characteristics, which plays a significant role in reducing mortality. Although a great breakthrough of the task of skin lesion classification has been made with the application of convolutional neural network, the inter-class similarity and intra-class variation in skin lesions images, the high class imbalance of the dataset and the lack of ability to focus on the lesion area all affect the classification results of the model. In order to solve these problems, on the one hand, we use the grouping of multi-scale attention blocks (GMAB) to extract multi-scale fine-grained features so as to improve the model's ability to focus on the lesion area. On the other hand, we adopt the method of class-specific loss weighting for the problem of category imbalance. In this paper, we propose a deep convolution neural network dermatoscopic image classification method based on the grouping of multi-scale attention blocks and class-specific loss weighting. We evaluated our model on the HAM10000 dataset, and the results showed that the ACC and AUC of the proposed method were 91.6% and 97.1% respectively, which can achieve good results in dermatoscopic classification tasks.

Mask-Aware Semi-Supervised Object Detection in Floor Plans

Research has been growing on object detection using semi-supervised methods in past few years. We examine the intersection of these two areas for floor-plan objects to promote the research objective of detecting more accurate objects with less labeled data. The floor-plan objects include different furniture items with multiple types of the same class, and this high inter-class similarity impacts the performance of prior methods. In this paper, we present Mask R-CNN-based semi-supervised approach that provides pixel-to-pixel alignment to generate individual annotation masks for each class to mine the inter-class similarity. The semi-supervised approach has a student–teacher network that pulls information from the teacher network and feeds it to the student network. The teacher network uses unlabeled data to form pseudo-boxes, and the student network uses both label data with the pseudo boxes and labeled data as the ground truth for training. It learns representations of furniture items by combining labeled and label data. On the Mask R-CNN detector with ResNet-101 backbone network, the proposed approach achieves a mAP of 98.8%, 99.7%, and 99.8% with only 1%, 5% and 10% labeled data, respectively. Our experiment affirms the efficiency of the proposed approach, as it outperforms the previous semi-supervised approaches using only 1% of the labels.

A comprehensive analysis of dermoscopy images for melanoma detection via deep CNN features

Melanoma is the fastest growing and most lethal cancer among all forms of skin cancer. Deep learning methods, mainly convolutional neural networks (CNNs) have recently brought considerable attention in detecting skin cancers from dermoscopy images. However, learning valuable features by these methods has been challenging due to the inadequate training data, inter-class similarity, and intra-class variation in the skin lesions. In addition, most of these methods demand a considerable amount of parameters to tune. To address these issues, we present an automated framework that extracts visual features from dermoscopy images using a pre-trained deep CNN model and then employs a set of classifiers to detect melanoma. Recently, few pre-trained CNN architectures have been employed to extract deep features from skin lesions. However, a comprehensive analysis of such features derived from a variety of CNN architectures has not yet been performed for melanoma classification. Therefore, in this paper, we investigate the effectiveness of deep features extracted from eight contemporary CNN models. Also, we explore the impact of boundary localization and normalization techniques on melanoma detection. The suggested approach is evaluated using four benchmark datasets: PH2, ISIC 2016, ISIC 2017 and HAM10000. Experimental outcomes indicate that the DenseNet-121 with multi-layer perceptron (MLP) achieves a higher performance in terms of accuracy of 98.33%, 80.47%, 81.16% and 81% on PH2, ISIC 2016, ISIC 2017 and HAM10000 datasets compared to other CNN models and state-of-the-art methods.

Transformer based on channel-spatial attention for accurate classification of scenes in remote sensing image

Recently, the scenes in large high-resolution remote sensing (HRRS) datasets have been classified using convolutional neural network (CNN)-based methods. Such methods are well-suited for spatial feature extraction and can classify images with relatively high accuracy. However, CNNs do not adequately learn the long-distance dependencies between images and features in image processing, despite this being necessary for HRRS image processing as the semantic content of the scenes in these images is closely related to their spatial relationship. CNNs also have limitations in solving problems related to large intra-class differences and high inter-class similarity. To overcome these challenges, in this study we combine the channel-spatial attention (CSA) mechanism with the Vision Transformer method to propose an effective HRRS image scene classification framework using Channel-Spatial Attention Transformers (CSAT). The proposed model extracts the channel and spatial features of HRRS images using CSA and the Multi-head Self-Attention (MSA) mechanism in the transformer module. First, the HRRS image is mapped into a series of multiple planar 2D patch vectors after passing to the CSA. Second, the ordered vector is obtained via the linear transformation of each vector, and the position and learnable embedding vectors are added to the sequence vector to capture the inter-feature dependencies at a distance from the generated image. Next, we use MSA to extract image features and the residual network structure to complete the encoder construction to solve the gradient disappearance problem and avoid overfitting. Finally, a multi-layer perceptron is used to classify the scenes in the HRRS images. The CSAT network is evaluated using three public remote sensing scene image datasets: UC-Merced, AID, and NWPU-RESISC45. The experimental results show that the proposed CSAT network outperforms a selection of state-of-the-art methods in terms of scene classification.

Image Information Contribution Evaluation for Plant Diseases Classification via Inter-Class Similarity

Combineingplant diseases identification and deep learning algorithm can achieve cost-effective prevention effect, and has been widely used. However, the current field of intelligent plant diseases identification still faces the problems of insufficient data and inaccurate classification. Aiming to resolve these problems, the present research proposes an image information contribution evaluation method based on the analysis of inter-class similarity. Combining this method with the active learning image selection strategy can provide guidance for the collection and annotation of intelligent identification datasets of plant diseases, so as to improve the recognition effect and reduce the cost. The method proposed includes two modules: the inter-classes similarity evaluation module and the image information contribution evaluation module. The images located on the decision boundary between high similarity classes will be analysis as high information contribution images, they will provide more information for plant diseases classification. In order to verify the effectiveness of this method, experiments were carried on the fine-grained classification dataset of tomato diseases. Experimental results confirm the superiority of this method compared with others. This research is in the field of plant disease classification. For the detection and segmentation, further research is advisable.

TBE-Net: A Three-Branch Embedding Network With Part-Aware Ability and Feature Complementary Learning for Vehicle Re-Identification

Vehicle re-identification (Re-ID) is one of the promising applications in the field of computer vision. Existing vehicle Re-ID methods mainly focus on global appearance features or pre-defined local region features, which have difficulties in handling inter-class similarities and intra-class differences among vehicles in various traffic scenarios. This paper proposes a novel end-to-end three-branch embedding network (TBE-Net) with feature complementary learning and part-aware ability. The proposed TBE-Net integrates complementary features, global appearance, and local region features into a unified framework for subtle feature learning, thereby obtaining more integral and diverse vehicle features to re-identify the vehicle from similar ones. The local region feature branch in the proposed TBE-Net contains an attention module that highlights the major differences among local regions by adaptively assigning large weights to the critical local regions and small weights to insignificant local regions, thereby enhancing the perception sensitivity of the network to subtle discrepancies. The complementary branch in the proposed TBE-Net exploits different pooling operations to obtain more comprehensive structural features and multi-granularity features as a supplement to the global appearance and local region features. The abundant features help accommodate the ever-changing critical local regions in vehicles’ images due to the sensors’ settings, such as the position and shooting angle of surveillance cameras. The extensive experiments on VehicleID and VeRi-776 datasets show that the proposed TBE-Net outperforms the state-of-the-art methods.

Geometry-Aware Graph Embedding Projection Metric Learning for Image Set Classification

By describing image sets as linear subspaces on the Grassmann manifold, image set classification has received persistent attention. Despite the success made so far, the unhelpfully intraclass diversity and interclass similarity remain two key challenges in finding an effective lower dimensional feature space for similarity measurement. To explore a feasible solution to these issues, we propose a geometry-aware graph embedding projection metric learning (GEPML) algorithm. The proposed approach first constructs the interclass and the intraclass similarity graphs on the Grassmann manifold, aiming to exploit the local structural information of the data manifold. Besides, we generalize the Euclidean collaborative representation mechanism to the Grassmann manifold to adaptively perform graph learning. Then, to learn the embedding mapping and the similarity metric jointly, we formulate the Grassmannian dimensionality reduction (GDR) problem into an elaborately designed metric learning regularization term. The proposed algorithm is appraised on five benchmarking data sets and the competitive experimental results demonstrate its feasibility and effectiveness.

Multiple Thermal Sensor Array Fusion Toward Enabling Privacy-Preserving Human Monitoring Applications

Human-centric applications of a single thermal sensor array (TSA) have performed extremely well in many areas. However, most of these works have not yet reached the real applicability stage of the Internet of Things (IoT) applications. The main limitation of deploying such systems on a large scale is the challenge of fusing multiple TSAs to cover a wide inspection area, e.g., smart homes, hospitals, and many other domestic environments. On the other hand, objects that appear in the low-resolution thermal images acquired from TSA have low intraclass variations and high interclass similarities, making the identification of the overlapping regions through matching a comparable template image in multiple images very difficult. This article proposes a motion-based approach to fuse multiple TSAs and learn the domestic environment layout to enable further human-centred IoT applications to run in the cloud. Besides, a privacy improvement on utilizing these sensors in IoT applications is proposed. The proposed approach is evaluated with comprehensive experiments on different sensor placements and domestic environment conditions. This article shows an average performance of 92.5% accuracy using various machine learning techniques and use case scenarios.

AI-CenterNet CXR: An artificial intelligence (AI) enabled system for localization and classification of chest X-ray disease.

Machine learning techniques have lately attracted a lot of attention for their potential to execute expert-level clinical tasks, notably in the area of medical image analysis. Chest radiography is one of the most often utilized diagnostic imaging modalities in medical practice, and it necessitates timely coverage regarding the presence of probable abnormalities and disease diagnoses in the images. Computer-aided solutions for the identification of chest illness using chest radiography are being developed in medical imaging research. However, accurate localization and categorization of specific disorders in chest X-ray images is still a challenging problem due to the complex nature of radiographs, presence of different distortions, high inter-class similarities, and intra-class variations in abnormalities. In this work, we have presented an Artificial Intelligence (AI)-enabled fully automated approach using an end-to-end deep learning technique to improve the accuracy of thoracic illness diagnosis. We proposed AI-CenterNet CXR, a customized CenterNet model with an improved feature extraction network for the recognition of multi-label chest diseases. The enhanced backbone computes deep key points that improve the abnormality localization accuracy and, thus, overall disease classification performance. Moreover, the proposed architecture is lightweight and computationally efficient in comparison to the original CenterNet model. We have performed extensive experimentation to validate the effectiveness of the proposed technique using the National Institutes of Health (NIH) Chest X-ray dataset. Our method achieved an overall Area Under the Curve (AUC) of 0.888 and an average IOU of 0.801 to detect and classify the eight types of chest abnormalities. Both the qualitative and quantitative findings reveal that the suggested approach outperforms the existing methods, indicating the efficacy of our approach.

PPsNet: An improved deep learning model for microsatellite instability high prediction in colorectal cancer from whole slide images

Background and ObjectiveRecent studies have shown that colorectal cancer (CRC) patients with microsatellite instability high (MSI-H) are more likely to benefit from immunotherapy. However, current MSI testing methods are not available for all patients due to the lack of available equipment and trained personnel, as well as the high cost of the assay. Here, we developed an improved deep learning model to predict MSI-H in CRC from whole slide images (WSIs). MethodsWe established the MSI-H prediction model based on two stages: tumor detection and MSI classification. Previous works applied fine-tuning strategy directly for tumor detection, but ignoring the challenge of vanishing gradient due to the large number of convolutional layers. We added auxiliary classifiers to intermediate layers of pre-trained models to help propagate gradients back through in an effective manner. To predict MSI status, we constructed a pair-wise learning model with a synergic network, named parameter partial sharing network (PPsNet), where partial parameters are shared among two deep convolutional neural networks (DCNNs). The proposed PPsNet contained fewer parameters and reduced the problem of intra-class variation and inter-class similarity. We validated the proposed model on a holdout test set and two external test sets. Results144 H&E-stained WSIs from 144 CRC patients (81 cases with MSI-H and 63 cases with MSI-L/MSS) were collected retrospectively from three hospitals. The experimental results indicate that deep supervision based fine-tuning almost outperforms training from scratch and utilizing fine-tuning directly. The proposed PPsNet always achieves better accuracy and area under the receiver operating characteristic curve (AUC) than other solutions with four different neural network architectures on validation. The proposed method finally achieves obvious improvements than other state-of-the-art methods on the validation dataset with an accuracy of 87.28% and AUC of 94.29%. ConclusionsThe proposed method can obviously increase model performance and our model yields better performance than other methods. Additionally, this work also demonstrates the feasibility of MSI-H prediction using digital pathology images based on deep learning in the Asian population. It is hoped that this model could serve as an auxiliary tool to identify CRC patients with MSI-H more time-saving and efficiently.

Skin lesion classification based on the VGG‐16 fusion residual structure

AbstractThe analysis of skin lesion images is challenging due to the high interclass similarity and intraclass variance. Therefore, improving the ability to automatically classify based on skin lesion images is necessary to help physicians classify skin lesions. We propose a network model based on the Visual Geometry Group Network (VGG‐16) fusion residual structure for the multiclass classification of skin lesions. based on the VGG‐16 network, we simplify and improve the network structure by adding a preprocessing layer (CBRM layer) and fusing the residual structure. We also use a hair removal algorithm and perform six data augmentation operations on a small number of skin lesion images to balance the total number of the seven skin lesions in the dataset. The model was evaluated on the ISIC2018 dataset. Experiments have shown that our network model achieves good classification performance, with a test accuracy rate of 88.14% and a macroaverage of 98%.