Deep learning methods for the plant disease detection platform

In the development of social economy and scientific and technological innovation, the image processing mode and classification model chosen by network technology platform is becoming more and more changeable, but in essence, it is necessary to obtain characteristic information in effective image recognition and choose high-quality network algorithm and processing technology to complete image processing and image classification. Therefore, on the basis of understanding the current research trend of computer image processing and image classification model methods, this paper conducts in-depth discussion on the image processing methods and image classification model training design with artificial intelligence as the core and takes the image classification model of transfer learning as an example for practical exploration. The final results show that the image processing method and image classification model based on artificial intelligence have strong performance advantages in practical application.KeywordsArtificial intelligenceImage processingImage classificationThe migration study

Recently, number of medical X-ray images being generated is increasing rapidly due to the advancements in radiological equipment in medical centres. Medical X-ray image classification techniques are needed for effective decision making in the healthcare sector. Since the traditional image classification models are ineffective to accomplish maximum X-ray image classification performance, deep learning (DL) models have emerged. In this study, an Arithmetic Optimization Algorithm with Deep Learning-Based Medical X-Ray Image Classification (AOADL-MXIC) model has been developed. The proposed AOADL-MXIC model investigates the available X-ray images for the identification of diseases. Initially, the AOADL-MXIC model executes the pre-processing step using the Gabor filtering (GF) technique to eliminate the presence of noise. In the next level, the Capsule Network (CapsNet) model is utilized to derive feature vectors from the input X-ray images. Furthermore, for optimizing the hyperparameters related to the CapsNet approach, the AOA is exploited. Finally, the bidirectional gated recurrent unit (BiGRU) model is employed for the classification of medical X-ray images. The experimental result analysis of the AOADL-MXIC technique on a set of medical images stated the promising performance over the other models.KeywordsX-ray imagesArithmetic optimization algorithmDeep learningFeature extractionHyperparameter tuning

Performance of deep learning models for classifying and detecting common weeds in corn and soybean production systems

Deep learning techniques specifically deep convolutional neural network (CNN) models, the latest core model of artificial neural networks, provide computer-vision capabilities, including medical and biomedical image analysis and classification. In this paper, we imaged ex vivo human tooth specimens using OCT imaging systems to classify and clarify the accuracy of different tooth samples with and without carious lesions via deep CNN models with transfer- learning and fine-tuning strategies. Collecting a large amount of OCT image data from dental samples can be difficult, and not providing sufficient data for CNN models can lead to overfitting. For these reasons, transfer learning and fine- tuning techniques were utilized in this study. OCT images of human extracted premolar and molar teeth were categorized into three classes. Five deep CNN models, specifically, a basic CNN with three convolutional and max pooling layers, VGG16 and VGG19 transfer-learning models, and finely tuned VGG16 and VGG19 models were developed and evaluated for OCT image classification of dental caries. In transfer learning, an existing learned model was employed as a feature extractor without changing the weight data, while in fine tuning, an existing learned model was utilized as a feature extractor by relearning some of the weight data. These methods are powerful methods for training deep CNN models without overfitting. This study highlights the performance of various deep learning models for OCT image classification of carious lesions.

Image Classification (IC) is most prominent among other Artificial Intelligence (AI) domains. Mainly, IC participates rigorously for the development of society in a variety of application areas such as finance, marketing, health, industrial automation, education, and safety and security. Typically, an IC model takes image input data and tunes itself as per the required application task and classify accordingly. Among the various categories of images, color image category is better due to the capability of capturing more details, which are essential for classification purpose. However, the modern world demands Realtime or online image classification, which involves Imagery Streams. The highly likely uncertainty in Imagery Streams is due to non-stationary environment, for example, certain features or class boundaries which are valid at one-time step are not adequate for another time step. These uncertainties in Imagery Streams have deleterious effects on IC models, which causes performance degradation in terms of accuracy or make IC models, not in further use. Therefore, to overcome these issues, IC models need to adapt to changes caused by uncertainties in Imagery Streams. This paper focuses on the understanding the possible scenarios of such uncertainties in Color Imagery Streams, investigates the deleterious effects due to changes in Color Imagery Streams and provides the possible mitigation approach to overcome the issues in IC models. The contribution of this research is the first step towards an adaptive model development to mitigate the deleterious effects of uncertainty in Color Imagery Streams. This model will benefit many application areas and will directly contribute to the daily life of a society.

Fused deep neural networks for sustainable and computational management of heat-transfer pipeline diagnosis

Deep learning-based image classification for online multi-coal and multi-class sorting

Image classification is widely used in different domains such as autonomous driving and medical care, etc. In this paper, we focus on using deep learning model to identify image. We also analysis the performance of CNN-based model and RNN-based model on image classification. Lots of relevant datasets are applied to image classification such as ImageNet dataset and MINIST dataset. In this paper, we conduct experiments on these two datasets. The experimental results of training and testing on those two datasets shows that CNN model is better than RNN model in image classification.

In the domain of general image forgery detection, a myriad of different classification solutions have been developed to distinguish a “tampered” image from a “pristine” image. In this work, we aim to develop a new method to tackle the problem of binary image forgery detection. Our approach builds upon the extensive training that state-of-the-art image classification models have undergone on regular images from the ImageNet dataset, and transfers that knowledge to the image forgery detection space. By leveraging transfer learning and fine tuning, we can fit state-of-the-art image classification models to the forgery detection task. We train the models on a diverse and evenly distributed image forgery dataset. With five models—EfficientNetB0, VGG16, Xception, ResNet50V2, and NASNet-Large—we transferred and adapted pre-trained knowledge from ImageNet to the forgery detection task. Each model was fitted, fine-tuned, and evaluated according to a set of performance metrics. Our evaluation demonstrated the efficacy of large-scale image classification models—paired with transfer learning and fine tuning—at detecting image forgeries. When pitted against a previously unseen dataset, the best-performing model of EfficientNetB0 could achieve an accuracy rate of nearly 89.7%.

Forest fires pose a significant threat to ecosystems, wildlife, property, and human lives worldwide. Leveraging advancements in artificial intelligence and machine learning, our research presents a comprehensive approach to forest fire detection and management. We employ a state-ofthe-art image classification model, YOLOv8, to swiftly identify fire occurrences within forest imagery, achieving high accuracy rates. Concurrently, we develop a predictive model using logistic regression to forecast the likelihood of fire outbreaks based on environmental factors. Integration of these technologies holds promise for proactive forest fire management. Future prospects include the integration of our YOLOv8 model with UAVs for real-time monitoring and early detection of fire occurrences, thus enhancing environmental conservation and safety measures.

Feature extraction is an important step in classification. It directly results in an improvement of classification performance. Recent successes of convolutional neural networks (CNN) have revolutionized image classification in computer vision. The outstanding convolution layer of CNN performs feature extraction to obtain promising features from images. However, it faces the overfitting problem and computational complexity due to the complicated structure of the convolution layer and deep computation. Therefore, this research problem is challenging. This paper proposes a novel deep feature extraction method based on a cellular automata (CA) model for image classification. It is established on the basis of a deep learning approach and multilayer CA with two main processes. Firstly, in the feature extraction process, multilayer CA with rules are built as the deep feature extraction model based on CA theory. The model aims at extracting multilayer features, called feature matrices, from images. Then, these feature matrices are used to generate score matrices for the deep feature model trained by the CA rules. Secondly, in the decision process, the score matrices are flattened and fed into the fully connected layer of an artificial neural network (ANN) for classification. For performance evaluation, the proposed method is empirically tested on BreaKHis, a popular public breast cancer image dataset used in several promising and popular studies, in comparison with the state-of-the-art methods. The experimental results show that the proposed method achieves the better results up to 7.95% improvement on average when compared with the state-of-the-art methods.

With the deep learning (DL) sweeping the world. Traditional image classification methods are difficult to process huge image data and cannot meet people's requirements for image classification accuracy and speed. The image classification method based on convolutional neural network (CNN) breaks through the bottle neck of traditional image classification methods and becomes the mainstream algorithm of image classification at present, how to effectively use convolutional neural network to classify images has become a hot research topic in the field of computer vision at home and abroad. Convolutional neural network (CNN) has performed well in image classification and segmentation, target detection and other applications, and its powerful feature learning and feature expression capabilities are increasingly respected by researchers. However, CNN still has a few problems, such as incomplete feature extraction and overfitting of sample training. In view of these problems, after in-depth research on the application of convolutional neural network in image processing, this paper gives the mainstream structure model, advantages and disadvantages, time/space complexity, problems that may be encountered in the model training process and corresponding solutions used in image classification based on convolutional neural network. Through the overview of the research status of CNN model in image classification, it provides suggestions for the further development and research direction of CNN.

Image classification is a part of computer vision, in which the digital system categorizes the entire image. Deep Learning (DL) models are widely used for image classification. However, creating DL models is resource-intensive and time-consuming, and requires extensive knowledge in the DL domain. Google Teachable Machines (GTM) is a website that outputs a trained model given the dataset, however, GTM uses only the MobileNet model and does not balance the image dataset which affects the model’s accuracy. This paper proposes a tool that automates the steps in building and training an image classification model. Using this tool does not require any extensive knowledge in DL. The tool automates the image data pre-processing steps, model building, model training, and model testing to output the best model for the given image classification dataset based on the test accuracy. The tool is tested on two datasets (each balanced and unbalanced dataset): a custom construction dataset and a Minet dataset. Both datasets are also used to train models using the GTM website. Due to the automated pre-processing steps, the average increase in the accuracy is 14.55% in the construction dataset and 3.91% in the Minet dataset. Comparing to the GTM models, the tool produced model with 8.33% more accuracy on the construction dataset and model with 14.07% more accuracy on the Minet dataset. The models trained by the proposed tool have better accuracy compared to the models obtained using GTM. Thus, using the image classification model selector facilitates the creation of an effective image classification model for the target dataset.

The advancement of automated medical diagnosis in biomedical engineering has become an important area of research. Image classification is one of the diagnostic approaches that do not require segmentation which can draw quicker inferences. The proposed non-invasive diagnostic support system in this study is considered as an image classification system where the given brain image is classified as normal or abnormal. The ability of deep learning allows a single model for feature extraction as well as classification whereas the rational models require separate models. One of the best models for image localization and classification is the Visual Geometric Group (VGG) model. In this study, an efficient modified VGG architecture for brain image classification is developed using transfer learning. The pooling layer is modified to enhance the classification capability of VGG architecture. Results show that the modified VGG architecture outperforms the conventional VGG architecture with a 5% improvement in classification accuracy using 16 layers on MRI images of the REpository of Molecular BRAin Neoplasia DaTa (REMBRANDT) database.

Image classification is a major application domain for conventional deep learning (DL). Quantum machine learning (QML) has the potential to revolutionize image classification. In any typical DL-based image classification, we use convolutional neural network (CNN) to extract features from the image and multi-layer perceptron network (MLP) to create the actual decision boundaries. QML models can be useful in both of these tasks. On one hand, convolution with parameterized quantum circuits (Quanvolution) can extract rich features from the images. On the other hand, quantum neural network (QNN) models can create complex decision boundaries. Therefore, Quanvolution and QNN can be used to create an end-to-end QML model for image classification. Alternatively, we can extract image features separately using classical dimension reduction techniques such as, Principal Components Analysis (PCA) or Convolutional Autoen-coder (CAE) and use the extracted features to train a QNN. We review two proposals on quantum-classical hybrid ML models for image classification namely, Quanvolutional Neural Network and dimension reduction using a classical algorithm followed by QNN. Particularly, we make a case for trainable filters in Quanvolution and CAE-based feature extraction for image datasets (instead of dimension reduction using linear transformations such as, PCA). We discuss various design choices, potential opportunities, and drawbacks of these models. We also release a Python-based framework to create and explore these hybrid models with a variety of design choices.