Graph-Collaborated Auto-Encoder Hashing for Multi-view Binary Clustering

Wang, Huibing; Yao, Maojin; Jiang, Guangqi; Mi, Zetian; Fu, Xianping

doi:10.48550/arxiv.2301.02484

Cited by 3 publications

(2 citation statements)

References 47 publications

(57 reference statements)

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“…In recent years and thanks to advances in DL, new algorithms have emerged that combine neural networks and perceptual hashes [39][40][41][42][43]. Perceptual hash functions can use techniques such as CNN to adaptively detect manipulation techniques and features.…”

Section: Strategies Based On Image Hash Databasementioning

confidence: 99%

Learning Strategies for Sensitive Content Detection

et al. 2023

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Currently, the volume of sensitive content on the Internet, such as pornography and child pornography, and the amount of time that people spend online (especially children) have led to an increase in the distribution of such content (e.g., images of children being sexually abused, real-time videos of such abuse, grooming activities, etc.). It is therefore essential to have effective IT tools that automate the detection and blocking of this type of material, as manual filtering of huge volumes of data is practically impossible. The goal of this study is to carry out a comprehensive review of different learning strategies for the detection of sensitive content available in the literature, from the most conventional techniques to the most cutting-edge deep learning algorithms, highlighting the strengths and weaknesses of each, as well as the datasets used. The performance and scalability of the different strategies proposed in this work depend on the heterogeneity of the dataset, the feature extraction techniques (hashes, visual, audio, etc.) and the learning algorithms. Finally, new lines of research in sensitive-content detection are presented.

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Section: Strategies Based On Image Hash Databasementioning

confidence: 99%

Learning Strategies for Sensitive Content Detection

et al. 2023

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“…This can result in unnecessary anxiety, inconvenient follow-up care, extra imaging tests, and sometimes a need for tissue sampling (often a needle biopsy) [5,6]. Additionally, machine learning techniques have the potential to improve the process of evaluating multiple-view radiology images based on graph-based clustering techniques [7][8][9][10]. Deep learning as a subset of machine learning in recent years has revolutionized the interpretation of diagnostic imaging studies [11].…”

Section: Introductionmentioning

confidence: 99%

Breast Cancer Detection in Mammography Images: A CNN-Based Approach with Feature Selection

Jafari

Karami

2023

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The prompt and accurate diagnosis of breast lesions, including the distinction between cancer, non-cancer, and suspicious cancer, plays a crucial role in the prognosis of breast cancer. In this paper, we introduce a novel method based on feature extraction and reduction for the detection of breast cancer in mammography images. First, we extract features from multiple pre-trained convolutional neural network (CNN) models, and then concatenate them. The most informative features are selected based on their mutual information with the target variable. Subsequently, the selected features can be classified using a machine learning algorithm. We evaluate our approach using four different machine learning algorithms: neural network (NN), k-nearest neighbor (kNN), random forest (RF), and support vector machine (SVM). Our results demonstrate that the NN-based classifier achieves an impressive accuracy of 92% on the RSNA dataset. This dataset is newly introduced and includes two views as well as additional features like age, which contributed to the improved performance. We compare our proposed algorithm with state-of-the-art methods and demonstrate its superiority, particularly in terms of accuracy and sensitivity. For the MIAS dataset, we achieve an accuracy as high as 94.5%, and for the DDSM dataset, an accuracy of 96% is attained. These results highlight the effectiveness of our method in accurately diagnosing breast lesions and surpassing existing approaches.

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Breast Cancer Detection in Mammography Images: A CNN-Based Approach with Feature Selection

Jafari¹,

Karami²

2023

Preprint

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The prompt and accurate diagnosis of breast lesions, including the distinction between cancer, non-cancer, and suspicious cancer, plays a crucial role in the prognosis of breast cancer. In this paper, we introduce a novel method based on feature extraction and reduction for detection of breast cancer in mammography images. First, we extract features from multiple pre-trained convolutional neural network (CNN) models, and then concatenate them. The most informative features are selected based on their mutual information with the target variable. Subsequently, the selected features can be classified using a machine learning algorithm. We evaluate our approach using four different machine learning algorithms, and our results demonstrate that the neural network-based classifier yields an accuracy as high as 92% for the RSNA dataset which is a new dataset that provides two views and additional features such as age. We compare our proposed algorithm with state-of-the-art methods and demonstrate its superiority, particularly in terms of accuracy and sensitivity. For the MIAS dataset, we achieve an accuracy as high as 94.5%, and for the DDSM dataset, an accuracy of 96% is attained. These results highlight the effectiveness of our method in accurately diagnosing breast lesions and surpassing existing approaches.

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Graph-Collaborated Auto-Encoder Hashing for Multi-view Binary Clustering

Cited by 3 publications

References 47 publications

Learning Strategies for Sensitive Content Detection

Learning Strategies for Sensitive Content Detection

Breast Cancer Detection in Mammography Images: A CNN-Based Approach with Feature Selection

Breast Cancer Detection in Mammography Images: A CNN-Based Approach with Feature Selection

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