CNN Based Target Classification in Vehicular Networks with Millimeter-Wave Radar

Medical images classification is a challenging research topic in the field of computer vision, especially when applied to diagnosis of breast cancer (BC). Nowadays, histopathological image is marked as the gold standard for diagnosing BC. However, such diagnosis is heavily dependent on the clinician's experience, which is extremely time consuming and is subjected to human error even for experienced doctors. To address those problems, this paper implements an automated method for distinguishing the benign from the malignant tumor based on a convolutional neural network (CNN). Traditional deep CNN and machine learning methods not only lead to poor performance, but also fail to make full use of the long-term dependence between some key features and image tags. To further meet the high accuracy requirement of diagnosis, according to the characteristics of histopathological images, we propose a novel CNN framework. Firstly, a normal image is augmented to solve the problem about having a limited database. Secondly, we introduce transfer learning to obtain more accurate weight parameters that were pre-trained on the ImageNet. Thirdly, we combine various features extracted by many individual models to obtain comprehensive features. Finally, random forest is introduced to enforce classification. The experimental results show that novel CNN frameworks have better performance compared with individual models, including DenseNet and ResNet. Experimental results are able to prove the effectiveness of our strategy.

Cloud classification of satellite image is the basis of meteorological forecast. Traditional machine learning methods need to manually design and extract a large number of image features, while the utilization of satellite image features is not high. This paper constructs a convolution neural network for cloud classification, which can automatically learn features and obtain classification results. The experimental results on the FY-2C satellite image show that the features extracted by deep convolution neural network are more favorable to the classification of satellite cloud. The performance of cloud classification based on deep convolution neural network is better than that of traditional machine learning methods. The method has high precision and good robustness.

Recently, deep learning has become a promising technique for constructing gesture recognition classifiers from surface electromyography (sEMG) signals in human–computer interaction. In this paper, we propose a gesture recognition method with sEMG signals based on a deep multi-parallel convolutional neural network (CNN), which solves the problem that traditional machine learning methods may lose too much useful information during feature extraction. CNNs provide an efficient way to constrain the complexity of feedforward neural networks by weight sharing and restriction to local connections. Sophisticated feature extraction is to be avoided and hand gestures are to be classified directly. A multi-parallel and multi-convolution layer convolution structure is proposed to classify hand gestures. Experiment results show that in comparison with five traditional machine learning methods, the proposed method could achieve higher accuracy.

Synthetic Aperture Radar (SAR) images are prone to be contaminated by noise, which makes it very difficult to perform target recognition in SAR images. Inspired by great success of very deep convolutional neural networks (CNNs), this paper proposes a robust feature extraction method for SAR image target classification by adaptively fusing effective features from different CNN layers. First, YOLOv4 network is fine-tuned to detect the targets from the respective MF SAR target images. Second, a very deep CNN is trained from scratch on the moving and stationary target acquisition and recognition (MSTAR) database by using small filters throughout the whole net to reduce the speckle noise. Besides, using small-size convolution filters decreases the number of parameters in each layer and, therefore, reduces computation cost as the CNN goes deeper. The resulting CNN model is capable of extracting very deep features from the target images without performing any noise filtering or pre-processing techniques. Third, our approach proposes to use the multi-canonical correlation analysis (MCCA) to adaptively learn CNN features from different layers such that the resulting representations are highly linearly correlated and therefore can achieve better classification accuracy even if a simple linear support vector machine is used. Experimental results on the MSTAR dataset demonstrate that the proposed method outperforms the state-of-the-art methods.

A well-known vegetable crop farmed all throughout the world is the potato. The possibility of a large number of diseases, however, could also impair the harvest. In order to quickly deal with the crop, it is vital for the planter to recognise the type of contamination. It had been determined that the leaves were a simple indicator of many diseases. Many Machine Learning (ML) techniques as well as Convolution Neural Network (CNN) models are superior in the literature for the detection of tomato crop diseases. CNN models rely on deep learning and neural networks, which are superior than traditional machine learning methods like decision trees and kNN. Although well before CNN creates beautiful artwork, the wide range of subjects they cover renders them operationally intensive. This painting suggests a less complex CNN model with eight convolution layer At the publicly accessible dataset PlantVillage, the recommended compact model performs better than the standard machine learning techniques and pre-trained fashions with an accuracy of 98.4%. The PlantVillage dataset includes 39 classes of different crops, including tomato diseases. These crops include apple, potato, maize, grapes, and more. In comparison to VGG16, which has an amazing accuracy of 93% 5% in pretrained patterns, k-NN has the absolute best accuracy of 94% in conventional ML approaches. After image augmentation, picture pre-processing was used to improve the suggested CNN's overall performance by having the ability to modify the brightness of the image using the knowledge of a prospective variable involving a randomized width of the image. The suggested model, which has a 98% accuracy rate, is also incredibly effective with datasets other than PlantVillage.

In Cassini ISS (Imaging Science Subsystem) images, contour detection is often performed on disk-resolved objects to accurately locate their center. Thus, contour detection is a key problem. Traditional edge detection methods, such as Canny and Roberts, often extract the contour with too much interior details and noise. Although the deep convolutional neural network has been applied successfully in many image tasks, such as classification and object detection, it needs more time and computer resources. In this paper, a contour detection algorithm based on H-ELM (Hierarchical Extreme Learning Machine) and DenseCRF (Dense Conditional Random Field) is proposed for Cassini ISS images. The experimental results show that this algorithm’s performance is better than both traditional machine learning methods, such as Support Vector Machine, Extreme Learning Machine and even deep Convolutional Neural Network. The extracted contour is closer to the actual contour. Moreover, it can be trained and tested quickly on the general configuration of PC, and thus can be applied to contour detection for Cassini ISS images.

To solve the problem of fault diagnosis of rolling bearing caused by large amount of data and difficulties of processing those data on to bearing set, based on Convolution Neural Network, a new method of data processing is proposed in this paper. With this method, one-dimensional time domain signal can be transformed into two-dimensional images, which is more suitable for Convolutional Neural Network processing. Meanwhile, the traditional machine learning method has the disadvantage of low robustness and low recognition rate with noise interference. Therefore, based on the feature extraction of Convolution Neural Network, in this paper we proposed an improved LeNet-5 Convolution Neural Network model, that is, adding a convolution layer and a pooling layer to the classic LeNet-5 model. The hidden layer features are extracted by using the trainable convolution kernel, while the extracted implicit features are reduced by the pooling layer, the Softmax classifier is used for classification and recognition of rolling bearing faults. In this paper we verified the effectiveness of the improved LeNet-5 model for fault diagnosis of rolling bearing by using the rolling bearing data to train the classic LeNet-5 model and the improved model.

Because bacterial blight (BB) disease seriously affects the yield and quality of rice, breeding BB resistant rice is an important priority for plant breeders but the process is time-consuming. The feasibility of using terahertz imaging technology and near-infrared hyperspectral imaging technology to identify BB resistant seeds has therefore been studied. The two-dimensional (2D) spectral images and one-dimensional (1D) spectra provided by both imaging methods were used to build discriminant models based on a deep learning method, the convolutional neural network (CNN), and traditional machine learning methods, support vector machine (SVM), random forest (RF), and partial least squares discriminant analysis (PLS-DA). The highest classification accuracy was achieved by the discriminate model based on CNN using the terahertz absorption spectra. Confusion matrixes were pictured to show the identification details. The t-distributed stochastic neighbor embedding (t-SNE) method was used to visualize the process of CNN data processing. Terahertz imaging technology combined with CNN has great potential to quickly identify BB resistant rice seeds and is more accurate than using near-infrared hyperspectral imaging.

Traditional machine learning methods like GLCM and ABCD rules have long been employed for image classification tasks. However, they come with inherent limitations, primarily the need for manual feature extraction. This manual feature extraction process is time-consuming and relies on expert domain knowledge, making it challenging for non-experts to use effectively. Deep learning methods, specifically Convolutional Neural Networks (CNN), have revolutionized image classification by automating the feature extraction. CNNs can learn hierarchical features directly from the raw pixel values, eliminating the need for manual feature engineering. Despite their powerful capabilities, CNNs have limitations, mainly when working with small image datasets. They may overfit the data or struggle to generalize effectively. In light of these considerations, this study adopts a hybrid approach that leverages the strengths of both deep learning and traditional machine learning. CNNs are automatic feature extractors, allowing the model to capture meaningful image patterns. These extracted features are then fed into a Support Vector Machine (SVM) classifier, known for its efficiency and effectiveness in handling small datasets. The results of this study are encouraging, with an accuracy of 0.94 and an AUC score of 0.94. Notably, these metrics outperform Abbas' previous research by a significant margin, underscoring the effectiveness of the hybrid CNN-SVM approach. This research reinforces that SVM classifiers are well-suited for tasks involving limited image data, yielding improved classification accuracy and highlighting the potential for broader applications in image analysis.

Convolutional Neural Network (CNN) is a deep learning algorithm that is widely used to identify and classify a digital image object. In this study the Convolutional Neural Network (CNN) is used as an algorithm that functions to identify leaf types (certain plants) based on images obtained from a public dataset provider named Daun Jamu Indonesia. The existence of image characteristics causes the assistance process to require a more detailed feature selection process. Therefore the CNN method is used in order to solve the problem. The Convolutional Neural Network (CNN) method is capable of performing image recognition by minimizing feature extraction. CNN is also reliable in processing unstructured data because it uses a multi-layered structure of artificial reasoning networks. The image recognition process is carried out by looking for the shape of the model that matches the processed data in order to get the best results. In this study, the augmentation process was carried out on the training data and validation data so that overfitting does not occur in the Convolutional Neural Network (CNN). The results obtained in this study indicate that the Convolutional Neural Network (CNN) method can identify leaf types with a measured accuracy rate of 92% using the Confusion Matrix evaluation method. It is hoped that this research can be used as a reference for the use of the Convolutional Neural Network (CNN) method for image data, especially plant leaf types.

Image recognition is one of the key technologies for worker’s helmet detection using an unmanned aerial vehicle (UAV). By analyzing the image feature extraction method for workers’ helmet detection based on convolutional neural network (CNN), a double-channel convolutional neural network (DCNN) model is proposed to improve the traditional image processing methods. On the basis of AlexNet model, the image features of the worker can be extracted using two independent CNNs, and the essential image features can be better reflected considering the abstraction degree of the features. Combining a traditional machine learning method and random forest (RF), an intelligent recognition algorithm based on DCNN and RF is proposed for workers’ helmet detection. The experimental results show that deep learning (DL) is closely related to the traditional machine learning methods. Moreover, adding a DL module to the traditional machine learning framework can improve the recognition accuracy.

Ultrasound (US) is the preferred modality for fatty liver disease diagnosis due to its noninvasive, real-time, and cost-effective imaging capabilities. However, traditional B-mode US is qualitative, and therefore, the assessment is very subjective. Computer-aided diagnostic tools can improve the specificity and sensitivity of US and help clinicians to perform uniform diagnoses. In this work, we propose a novel deep learning model for nonalcoholic fatty liver disease classification from US data. We design a multi-feature guided multi-scale residual convolutional neural network (CNN) architecture to capture features of different receptive fields. B-mode US images are combined with their corresponding local phase filtered images and radial symmetry transformed images as multi-feature inputs for the network. Various fusion strategies are studied to improve prediction accuracy. We evaluate the designed network architectures on B-mode in vivo liver US images collected from 55 subjects. We also provide quantitative results by comparing our proposed multi-feature CNN architecture against traditional CNN designs and machine learning methods. Quantitative results show an average classification accuracy above 90% over tenfold cross-validation. Our proposed method achieves a 97.8% area under the ROC curve (AUC) for the patient-specific leave-one-out cross-validation (LOOCV) evaluation. Comprehensive validation results further demonstrate that our proposed approaches achieve significant improvements compared to training mono-feature CNN architectures ([Formula: see text]). Feature combination is valuable for the traditional classification methods, and the use of multi-scale CNN can improve liver classification accuracy. Based on the promising performance, the proposed method has the potential in practical applications to help radiologists diagnose nonalcoholic fatty liver disease.

We address the Lithuanian author profiling task in two dimensions (AGE and GENDER) using two deep learning methods (i.e., Long Short-Term Memory - LSTM) and Convolutional Neural Network - CNN) applied on the top of Lithuanian neural word embeddings. We also investigate an impact of the training dataset size on the author profiling accuracy. The best results are achieved with the largest datasets, containing 5,000 instances in each class. Besides, LSTM was more effective on the smaller datasets, and CNN - on the larger ones. We compare the deep learning methods with the traditional machine learning methods (in particular, Naive Bayes Multinomial and Support Vector Machine), and frequencies of elements as the feature representation). The comparison revealed that the deep learning is not the best solution for our author profiling task.

Since the concept of the generative adversarial networks (GAN) was put forward in 2014, it has attracted great attention from researchers to continuously improve and apply. With the advent of the era of big data, the structure of deep convolutional neural networks has become more complex, and the feature learning and feature expression capabilities have improved compared with traditional machine learning methods. As a newly emerging research direction, vehicle re-identification (ReID) is of great significance. However, because of the images that can be obtained in practical applications often encounter artificially intentional or occlusion due to conditional constraints and characteristics of vehicle re-identification, the existing recognition rate is still not satisfactory. In order to solve this problem, this paper uses the adversarial idea in the generative adversarial networks combined the convolutional neural networks (CNN) to improve the baseline algorithm. In this paper, a lot of experiments are carried out on the standard dataset Vehicle ID. By comparing and analyzing the experimental results with the existing baseline algorithm, the proposed method embodies better recognition performance.

In recent years, CNNs (Convolutional Neural Networks) with their powerful feature representation and feature learning capabilities, have played an important role in gesture recognition tasks based on sparse multichannel surface EMG signals. As each muscle group in the upper limb plays a different role in a particular hand movement, we propose a hybrid CNN model that considers the spatial distribution of muscle groups in the myoelectric channel to improve the accuracy of hand gesture recognition. The model takes the spectrogram of CWT (Continuous Wavelet Transform) as input, based on the spatial distribution of channels, decomposes all channels into multiple input streams, lets the CNN learn the features of each stream separately, and gradually fuses (slowly fusion) the features learned by each stream, and then performs gesture classification. Finally, the results of several of these stream-division methods are fused for decision making to obtain classification accuracies. The proposed model was validated and tested on the Nina Pro DB4 dataset, and the average accuracy was improved compared to both traditional machine learning methods and multi-stream CNN models that do not take into account the spatial distribution of channels.