Dermoscopy Image Classification Based on StyleGAN and DenseNet201

Zhao, Chen; Shuai, Renjun; Ma, Li; Liu, Wenjia; Hu, Die; Wu, Menglin

doi:10.1109/access.2021.3049600

Cited by 68 publications

(42 citation statements)

References 46 publications

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“…For classification purposes [ 22 ], 2 dense layers with neurons are enclosed. The feature extraction with sigmoid activation function and DenseNet-201 is used for calculating dual classifications, with softmax activation function used as conventional DenseNet-201 architecture.…”

Section: The Proposed Modelmentioning

confidence: 99%

Hybrid Rider Optimization with Deep Learning Driven Biomedical Liver Cancer Detection and Classification

Duhayyim

Mengash

Marzouk

et al. 2022

Computational Intelligence and Neuroscience

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Biomedical engineering is the application of the principles and problem-solving methods of engineering to biology along with medicine. Computation intelligence is the study of design of intelligent agents which are systems acting perceptively. The computation intelligence paradigm offers more advantages to the enhancement and maintenance of the field of biomedical engineering. Liver cancer is the major reason of mortality worldwide. Earlier-stage diagnosis and treatment might increase the survival rate of liver cancer patients. Manual recognition of the cancer tissue is a time-consuming and difficult task. Hence, a computer-aided diagnosis (CAD) is employed in decision making procedures for accurate diagnosis and effective treatment. In contrast to classical image-dependent “semantic” feature evaluation from human expertise, deep learning techniques could learn feature representation automatically from sample images using convolutional neural network (CNN). This study introduces a Hybrid Rider Optimization with Deep Learning Driven Biomedical Liver Cancer Detection and Classification (HRO-DLBLCC) model. The proposed HRO-DLBLCC model majorly focuses on the identification of liver cancer in the medical images. To do so, the proposed HRO-DLBLCC model employs preprocessing in two stages, namely, Gabor filtering (GF) based noise removal and watershed transform based segmentation. In addition, the proposed HRO-DLBLCC model involves NAdam optimizer with DenseNet-201 based feature extractor to generate an optimal set of feature vectors. Finally, the HRO algorithm with recurrent neural network–long short-term memory (RNN-LSTM) model is applied for liver cancer classification, in which the hyperparameters of the RNN-LSTM model are tuned by the use of HRO algorithm. The HRO-DLBLCC model is experimentally validated and compared with existing models. The experimental results assured the promising performance of the HRO-DLBLCC model over recent approaches.

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Section: The Proposed Modelmentioning

confidence: 99%

Hybrid Rider Optimization with Deep Learning Driven Biomedical Liver Cancer Detection and Classification

Duhayyim

Mengash

Marzouk

et al. 2022

Computational Intelligence and Neuroscience

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“…Due to its high accuracy in many fields, deep learning has become the most advanced machine learning technology. Deep learning and CNNs have been successfully used in breast cancer detection [ 7 ], skin cancer recognition [ 73 ], and COVID-19 recognition and analysis [ 26 ]. Among them, there are many studies based on convolutional neural networks, and convolutional neural networks (CNNs) have been the standard for 3D medical image classification and segmentation.…”

Section: Introductionmentioning

confidence: 99%

Improving cervical cancer classification with imbalanced datasets combining taming transformers with T2T-ViT

Zhao

Shuai

et al. 2022

Multimed Tools Appl

Self Cite

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Cervical cell classification has important clinical significance in cervical cancer screening at early stages. However, there are fewer public cervical cancer smear cell datasets, the weights of each classes’ samples are unbalanced, the image quality is uneven, and the classification research results based on CNN tend to overfit. To solve the above problems, we propose a cervical cell image generation model based on taming transformers (CCG-taming transformers) to provide high-quality cervical cancer datasets with sufficient samples and balanced weights, we improve the encoder structure by introducing SE-block and MultiRes-block to improve the ability to extract information from cervical cancer cells images; we introduce Layer Normlization to standardize the data, which is convenient for the subsequent non-linear processing of the data by the ReLU activation function in feed forward; we also introduce SMOTE-Tomek Links to balance the source data set and the number of samples and weights of the images we use Tokens-to-Token Vision Transformers (T2T-ViT) combing transfer learning to classify the cervical cancer smear cell image dataset to improve the classification performance. Classification experiments using the model proposed in this paper are performed on three public cervical cancer datasets, the classification accuracy in the liquid-based cytology Pap smear dataset (4-class), SIPAKMeD (5-class), and Herlev (7-class) are 98.79%, 99.58%, and 99.88%, respectively. The quality of the images we generated on these three data sets is very close to the source data set, the final averaged inception score (IS), Fréchet inception distance (FID), Recall and Precision are 3.75, 0.71, 0.32 and 0.65 respectively. Our method improves the accuracy of cervical cancer smear cell classification, provides more cervical cell sample images for cervical cancer-related research, and assists gynecologists to judge and diagnose different types of cervical cancer cells and analyze cervical cancer cells at different stages, which are difficult to distinguish. This paper applies the transformer to the generation and recognition of cervical cancer cell images for the first time. Supplementary Information The online version contains supplementary material available at 10.1007/s11042-022-12670-0.

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“…Generative neural networks (GNNs) are a class of deep neural network models that represent the state-of-the-art technique that can be leveraged to democratize the mass synthesis of manipulated digital content 21,22 . The have been used to fabricate images by training them to encode human features 23 , to manipulate images via replacing speci c components of a digital image or video 24 , and to create videos via animation of a still image with the characteristics of a source video 25 .…”

Section: Introductionmentioning

confidence: 99%

Deepfakes and Scientific Knowledge Dissemination

Doss

Monschein

Shu

et al. 2022

Preprint

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Science misinformation on topics ranging from climate change to vaccines have significant public policy repercussions. Artificial intelligence-based methods of altering videos and photos (deepfakes) lower the barriers to the mass creation and dissemination of realistic, manipulated digital content. The risk of exposure to deepfakes among education stakeholders has increased as learners and educators rely on videos to obtain and share information. We detail the results of the first study to understand the vulnerabilities of education stakeholders to science deepfakes and the characteristics that moderate vulnerability. We ground our study in climate change and survey individuals from five populations spanning students, educators, and the adult public. Our sample is nationally representative of three populations. We found that 33% to 50% of individuals cannot distinguish between authentic videos and deepfakes. All populations exhibit vulnerability to deepfakes, but vulnerability varies with age, political orientation, and trust in information sources. Adults and educators exhibit greater vulnerability compared to students, indicating that those providing education are especially susceptible. Vulnerability increases with more exposure to potential deepfakes, suggesting that deepfakes become more pernicious without interventions. Our results suggest that focusing on the social context in which deepfakes reside is one promising strategy for combatting deepfakes.

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Dermoscopy Image Classification Based on StyleGAN and DenseNet201

Cited by 68 publications

References 46 publications

Hybrid Rider Optimization with Deep Learning Driven Biomedical Liver Cancer Detection and Classification

Hybrid Rider Optimization with Deep Learning Driven Biomedical Liver Cancer Detection and Classification

Improving cervical cancer classification with imbalanced datasets combining taming transformers with T2T-ViT

Deepfakes and Scientific Knowledge Dissemination

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