Diabetic Retinopathy (DR) is an eye disease associated with chronic diabetes. It remains the primary cause of visual impairment and blindness among the global working-age population. Early detection of DR is crucial f...
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ISBN:
(纸本)9783031821554;9783031821561
Diabetic Retinopathy (DR) is an eye disease associated with chronic diabetes. It remains the primary cause of visual impairment and blindness among the global working-age population. Early detection of DR is crucial for ensuring timely diagnosis and effective treatment. This paper proposes a new homogeneous ensemble-based approach constructed using a set of hybrid architectures as base learners and two combination rules (weighted and hard voting) for referable DR detection, using fundus images from the Messidor-2, Kaggle DR, and APTOS datasets. The hybrid architectures are created using deep feature extraction techniques, dimensionality reduction techniques to reduce the size of the extracted features, and a decision tree algorithm (DT) for classification. The results showed the potential of the proposed new approach which achieved high accuracy values over the three datasets: 90.65%, 93.01%, and 83.32% using the APTOS, Kaggle DR, and Messidor-2 datasets respectively. Therefore, we recommend using the proposed approach since it is impactful for referable DR classification, and it represents a promising tool to assist ophthalmologists in diagnosing DR.
In the new period, with the continuous development of society, urban management should also keep pace with the times, take the initiative to learn new ideas, introduce new technologies, and innovate and optimize in th...
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This research presents a comprehensive comparative analysis of various pre-trained backbone models and machinelearning techniques for output layers in convolutional neural networks (CNNs) applied to the classificatio...
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ISBN:
(纸本)9798350368918
This research presents a comprehensive comparative analysis of various pre-trained backbone models and machinelearning techniques for output layers in convolutional neural networks (CNNs) applied to the classification of circular objects with high similarity. Given the challenges associated with distinguishing visually similar objects, the study evaluates a diverse set of backbone models, including AlexNet, ConvNeXt (Base, Large, Small, Tiny), DenseNet (121, 161, 169, 201), EfficientNet (B0 through B7), EfficientNet-V2 (L, M, S), GoogLeNet, Inception-V3, and MaxViT-T. For the output layers, six machinelearning models-Decision Tree, K-Nearest Neighbors (KNN), Logistic Regression, Naive Bayes, Random Forest, and Support Vector machine (SVM)-are tested to identify their effectiveness in conjunction with the backbone models. The methodology involves fine-tuning the backbone models on a dataset of circular objects and training each machinelearning model on the features extracted by these backbones. Performance is evaluated using metrics such as accuracy, precision, recall, and F1-score, with cross-validation ensuring robust results. The analysis reveals that ConvNeXt Large and EfficientNet-B5 are the most effective backbone models for this task, offering superior feature extraction capabilities and consistent performance across all metrics. Among the machinelearning models, Logistic Regression and Random Forest demonstrate the highest performance, providing reliable and accurate classification. The optimal combination for this classification task is found to be ConvNeXt Large or EfficientNet-B5 as the backbone model with Logistic Regression or Random Forest as the output layer. This combination offers the best balance of classification accuracy and processing efficiency, making it ideal for automated systems dealing with circular objects with high similarity. The findings offer valuable insights for optimizing image classification systems in industrial applications,
Agriculture has an essential role in the national economic development structure. It is necessary to detect plant diseases as early as possible to prevent losses to increase production yields. Recent advances in image...
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Endothelial cells of the aorta are an excellent model system for studying inflammatory responses, angiogenesis, atherosclerosis, blood clotting, vascular contraction, and vasodilation. Cell counting is necessary when ...
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This research focuses on the detection of diseases on apple tree branches using artificial intelligence (AI). The aim of this study is to develop an intelligent system that can automatically detect and segment disease...
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Outbreaks Cardiovascular diseases (CVDs) are still the main reason behind deaths globally;because they have a great effect on the total disease burden. As of recent estimates, approximately 32 percent of all global de...
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In this research paper, our main focus is to design and develop a system for classification and recognition methodology for the acknowledgment and retrieval of a Sunflower flower in the natural environment centralized...
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In this research paper, our main focus is to design and develop a system for classification and recognition methodology for the acknowledgment and retrieval of a Sunflower flower in the natural environment centralized on the indigenous habitat dependent on a multi-layer method. Further, we design applica-tions for their better classification. To handle a difficult undertaking task, an interdisciplinary cooperation is displayed dependent in the latest advancement methods in software implementation in engineering and innovation implemented by machinelearning. A proposed work is design to increase the strategy for utilizing the techniques of machinelearning. Final utilization of the Texture Feature, Rst-Invariant Feature, pattern Classification and furthermore utilize the K-Closest Neighbor calculations is done. Firstly, the paper is proposes to study about how to gather a flower images from the natural environment along with their corresponding background and Secondly, the paper focus on the Sunflower classification utility through machinelearning. The computerization methods through blossom utilizing through AI system for sunflower utilized the 6-types of sunflower to get the fine yielding of profoundly sprouted sunflower blooms is caught from an advanced camera with a picture. The process of recognition imple-mented carried with 280 pictures. This method used a recognition as well as classification of sunflower by using the k-nearest neighbor image having overall 88.52% accuracy. This designed research paper, we trained the model with information and when concealed information is achieved then the predictive model predicts the Sunflower recognition through trained data supervised technique with machinelearning. (c) 2021 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the 1stinternational Con-ference on Computations in Materials and Applied Engineering - 2021.
Computer vision and natural language processing (NLP) are used in artificial intelligence (AI) to automatically create the description of contents of an image. A regenerative neuronal model is developed for this purpo...
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This paper proposes a methodology to automate the quality control of rice grain, as its been manually analysed by veteran rice inspector which are inaccurate, collection of data of various rice types to analyse qualit...
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