Developments in Image Processing Using Deep Learning and Reinforcement Learning

Valente, Jorge; António, João; Mora, Carlos; Jardim, Sandra

doi:10.3390/jimaging9100207

Cited by 31 publications

(8 citation statements)

References 119 publications

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“…The second approach utilizes neural networks to analyze the content of the image for pattern recognition. This method is particularly useful in identifying contextual patterns within images [26][27][28][29][30][31][32][33].…”

Section: Literature Reviewmentioning

confidence: 99%

Graph convolution networks for social media trolls detection use deep feature extraction

Asif,

Al-Razgan,

Ali

et al. 2024

J Cloud Comp

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This study presents a novel approach to identifying trolls and toxic content on social media using deep learning. We developed a machine-learning model capable of detecting toxic images through their embedded text content. Our approach leverages GloVe word embeddings to enhance the model's predictive accuracy. We also utilized Graph Convolutional Networks (GCNs) to effectively analyze the intricate relationships inherent in social media data. The practical implications of our work are significant, despite some limitations in the model's performance. While the model accurately identifies toxic content more than half of the time, it struggles with precision, correctly identifying positive instances less than 50% of the time. Additionally, its ability to detect all positive cases (recall) is limited, capturing only 40% of them. The F1-score, which is a measure of the model's balance between precision and recall, stands at around 0.4, indicating a need for further refinement to enhance its effectiveness. This research offers a promising step towards more effective monitoring and moderation of toxic content on social platforms.

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Section: Literature Reviewmentioning

confidence: 99%

Graph convolution networks for social media trolls detection use deep feature extraction

Asif,

Al-Razgan,

Ali

et al. 2024

J Cloud Comp

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“…Despite the significant advances in the field of Machine Learning, particularly in Deep Learning and its application to image processing [ 91 ], including image retrieval, several approaches based on more conventional techniques continue to be proposed. Some of these approaches use the feature fusion process to increase the accuracy of the system, such as the one proposed by Alsmadi.…”

Section: Related Workmentioning

confidence: 99%

DarwinGSE: Towards better image retrieval systems for intellectual property datasets

António,

Valente,

Mora

et al. 2024

PLoS ONE

Self Cite

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A trademark’s image is usually the first type of indirect contact between a consumer and a product or a service. Companies rely on graphical trademarks as a symbol of quality and instant recognition, seeking to protect them from copyright infringements. A popular defense mechanism is graphical searching, where an image is compared to a large database to find potential conflicts with similar trademarks. Despite not being a new subject, image retrieval state-of-the-art lacks reliable solutions in the Industrial Property (IP) sector, where datasets are practically unrestricted in content, with abstract images for which modeling human perception is a challenging task. Existing Content-based Image Retrieval (CBIR) systems still present several problems, particularly in terms of efficiency and reliability. In this paper, we propose a new CBIR system that overcomes these major limitations. It follows a modular methodology, composed of a set of individual components tasked with the retrieval, maintenance and gradual optimization of trademark image searching, working on large-scale, unlabeled datasets. Its generalization capacity is achieved using multiple feature descriptions, weighted separately, and combined to represent a single similarity score. Images are evaluated for general features, edge maps, and regions of interest, using a method based on Watershedding K-Means segments. We propose an image recovery process that relies on a new similarity measure between all feature descriptions. New trademark images are added every day to ensure up-to-date results. The proposed system showcases a timely retrieval speed, with 95% of searches having a 10 second presentation speed and a mean average precision of 93.7%, supporting its applicability to real-word IP protection scenarios.

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“…Enhanced cataract state classification and detection are crucial for precise diagnosis and treatment, potentially improving patient outcomes and healthcare efficiency. Convolutional neural networks (CNNs) are commonly used for image classification and detection, benefiting from data preparation, model selection, and augmentation for improved generalization [17,18].…”

Section: Introductionmentioning

confidence: 99%

CSDNet: A Novel Deep Learning Framework for Improved Cataract State Detection

P.L,

Vaddi,

Elish

et al. 2024

Diagnostics

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Cataracts, known for lens clouding and being a common cause of visual impairment, persist as a primary contributor to vision loss and blindness, presenting notable diagnostic and prognostic challenges. This work presents a novel framework called the Cataract States Detection Network (CSDNet), which utilizes deep learning methods to improve the detection of cataract states. The aim is to create a framework that is more lightweight and adaptable for use in environments or devices with limited memory or storage capacity. This involves reducing the number of trainable parameters while still allowing for effective learning of representations from data. Additionally, the framework is designed to be suitable for real-time or near-real-time applications where rapid inference is essential. This study utilizes cataract and normal images from the Ocular Disease Intelligent Recognition (ODIR) database. The suggested model employs smaller kernels, fewer training parameters, and layers to efficiently decrease the number of trainable parameters, thereby lowering computational costs and average running time compared to other pre-trained models such as VGG19, ResNet50, DenseNet201, MIRNet, Inception V3, Xception, and Efficient net B0. The experimental results illustrate that the proposed approach achieves a binary classification accuracy of 97.24% (normal or cataract) and an average cataract state detection accuracy of 98.17% (normal, grade 1—minimal cloudiness, grade 2—immature cataract, grade 3—mature cataract, and grade 4—hyper mature cataract), competing with state-of-the-art cataract detection methods. The resulting model is lightweight at 17 MB and has fewer trainable parameters (175, 617), making it suitable for deployment in environments or devices with constrained memory or storage capacity. With a runtime of 212 ms, it is well-suited for real-time or near-real-time applications requiring rapid inference.

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Developments in Image Processing Using Deep Learning and Reinforcement Learning

Cited by 31 publications

References 119 publications

Graph convolution networks for social media trolls detection use deep feature extraction

Graph convolution networks for social media trolls detection use deep feature extraction

DarwinGSE: Towards better image retrieval systems for intellectual property datasets

CSDNet: A Novel Deep Learning Framework for Improved Cataract State Detection

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