Special section editors Johan Debayle, Wolfgang Osten, and Dmitry Nikolaev introduce the Special Section on machinevision: Systems, Methods, and applications.
Special section editors Johan Debayle, Wolfgang Osten, and Dmitry Nikolaev introduce the Special Section on machinevision: Systems, Methods, and applications.
Retinopathy is a common complication of diabetes that can cause severe vision loss if not detected and managed promptly. In this study, we propose a comprehensive approach that leverages imageprocessing techniques to...
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ISBN:
(纸本)9781510665651;9781510665644
Retinopathy is a common complication of diabetes that can cause severe vision loss if not detected and managed promptly. In this study, we propose a comprehensive approach that leverages imageprocessing techniques to analyze fundus images of patients with diabetic retinopathy. Our primary focus is on vein extraction and hemorrhage detection, with exudate detection being performed only on specific images to showcase advancements in the current prototype algorithm. The dataset used in this project consists of images obtained from Mexican ophthalmology institutes, ensuring its relevance and applicability to the local population. By extracting veins and hemorrhages, we aim to capture crucial features indicative of the severity of retinopathy. These generated images, along with the original dataset, are utilized to train convolutional neural network (CNN) models, enabling accurate classification of the disease's degree into three categories. The significance of this project lies in its potential to serve as an auxiliary tool in diagnosing diabetic retinopathy. By automating the analysis of fundus images and providing objective classification results, our algorithm aims to assist healthcare professionals in making informed decisions regarding treatment and management options. The proposed method can potentially enhance the efficiency and precision of diabetic retinopathy (DR) diagnosis, improving Mexican health outcomes.
Dynamic Contrast Enhancement MRI (DCE-MRI) has become an essential tool for detecting breast cancer in recent years. However, the shape and size of lesions vary widely, and the boundary of lesions is blurry. This work...
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ISBN:
(纸本)9781665464680
Dynamic Contrast Enhancement MRI (DCE-MRI) has become an essential tool for detecting breast cancer in recent years. However, the shape and size of lesions vary widely, and the boundary of lesions is blurry. This work proposes a multi-scale attention-based v-Net (MSA-vNet) for DCE-MRI lesion segmentation, to address these issues. MSA-vNet, based on v-Net, initially employs the 3D multi-receptive-field feature extraction module, which includes multi-branch residual structure, atrous convolutions, and instance normalization layers. Second, to replace the long-range skip connection structure in v-Net, an attention-based long-range skip module is proposed. Finally, the Focal Toversky loss function is introduced in MSA-vNet to enable the model to focus on tiny lesions. The experiments on the breast cancer DCE-MRI dataset show that, the proposed method outperforms the state-of-the-art methods.
The use of medical images is an important link in the early detection of cancer. Artificial intelligence theories and technologies such as machine learning and neural networks have gradually become important tools to ...
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ISBN:
(纸本)9798350373820
The use of medical images is an important link in the early detection of cancer. Artificial intelligence theories and technologies such as machine learning and neural networks have gradually become important tools to assist in solving the classification problems of pathological images. However, traditional methods ignore the irreversible cost caused by misdiagnosis when the tumor is classified as other types in the prediction process, and it is difficult to obtain a classifier with high robustness for all data sources due to the differences in the manipulations of different operators in the process of slicing, staining and extracting small images. In this paper, we construct a cascade ensemble learning algorithm BDD-Boost (Based on Data Domin-Boost). A pathological image classification method of cascading multiple classifiers is proposed and used. Through the feature extraction and processing on the neurons of the preorder classifier, the adaptive threshold was generated to divide the data set. The postorder classifier was trained on the specified image set depending on the data set division result of the preorder classifier. Finally, the optimization results were obtained by integration. We verify the effectiveness of our algorithm by comparing experiments without BDD-Boost ensemble learning algorithm and with BDD-Boost ensemble learning algorithm. BDD-Boost improves the recognition performance by 1% on MobileNet_v2 and 2.2% on ResNet50. In the comparison of voting method and Bagging, it also shows better performance. The model proposed in this paper combines the advantages and characteristics of various convolutional neural networks, and has better performance and reliability than using a single convolutional neural network to directly predict the classification results of medical images under the condition of reducing computing resource consumption and calculation time. It can be used for computer-aided diagnosis and provide diagnostic reference for medical staff
Leaf diseases are a major danger to the world's food security and have a substantial impact on crop productivity. Effective plant disease management depends on the early identification and precise diagnosis of the...
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The evolution of low-cost embedded systems is growing exponentially;likewise, their use in robotics applications aims to achieve critical task execution by implementing sophisticated control and computer vision algori...
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The evolution of low-cost embedded systems is growing exponentially;likewise, their use in robotics applications aims to achieve critical task execution by implementing sophisticated control and computer vision algorithms. We review the state-of-the-art strategies available for Tiny machine Learning (TinyML) implementation to provide a complete overview using various existing embedded vision and control systems. Our discussion divides the article into four critical aspects that high-cost and low-cost embedded systems must include to execute real-time control and imageprocessing tasks, applying TinyML techniques: Hardware Architecture, vision System, Power Consumption, and Embedded Software Platform development environment. The advantages and disadvantages of the reviewed systems are presented. Subsequently, the perspectives of them for the next ten years are present. A basic TinyML implementation for embedded vision application using three low-cost embedded systems, Raspberry Pi Pico, ESP32, and Arduino Nano 33 BLE Sense, is presented for performance analysis.
In the production of power cables, the performance test of the cable insulation sheath is an important part. Compared with traditional testing methods, machinevision has the advantages of stable operation, high preci...
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Fast malignant melanoma tumor identification is vital for successful therapy. Melanoma has now been universally acknowledged to be the most lethal form of malignancy over all else since, even if not caught and address...
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Soybean is a major economic crop worldwide. So proper disease control measures must be implemented to reduce losses. These diseases can significantly affect the yield and quality of soybeans. machinevision and patter...
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With the rapid development of artificial intelligence, machine learning applications in visual design have become increasingly widespread, particularly in the field of imageprocessing. This study presents an intellig...
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ISBN:
(数字)9798350355413
ISBN:
(纸本)9798350355420
With the rapid development of artificial intelligence, machine learning applications in visual design have become increasingly widespread, particularly in the field of imageprocessing. This study presents an intelligent image generation platform based on Generative Adversarial Networks (GANs) aimed at improving design efficiency and creative expression. The platform integrates Deep Convolutional GANs (DCGANs) with style transfer techniques to efficiently generate high-quality images with artistic and visual appeal. Experimental results demonstrate that the model outperforms traditional methods in terms of clarity (PSNR of 28.4 dB), realism (Inception Score of 7.5), and style consistency (Style Loss of 0.0025). User experience evaluations indicate that designers rate the platform highly for ease of use and image generation quality. This model not only enhances design efficiency but also serves as a powerful tool for creative visual tasks, advancing the automation and intelligence of the design process.
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