4.6 Article

Skin Lesion Synthesis and Classification Using an Improved DCGAN Classifier

Related references

Note: Only part of the references are listed.
Article Computer Science, Artificial Intelligence

Analysis of the ISIC image datasets: Usage, benchmarks and recommendations

Bill Cassidy et al.

Summary: The ISIC datasets are important resources for researchers in the field of medical image analysis, particularly in skin cancer detection. Analysis revealed a significant number of duplicate images, leading to a proposed duplicate removal strategy and curated dataset recommendation for researchers.

MEDICAL IMAGE ANALYSIS (2022)

Article Chemistry, Analytical

Fine-Tuned DenseNet-169 for Breast Cancer Metastasis Prediction Using FastAI and 1-Cycle Policy

Adarsh Vulli et al.

Summary: This study introduces a new method for automated diagnosis and detection of metastases in breast cancer using the Fast AI framework and the 1-cycle policy, and compares it with previous methods. The proposed model has achieved an accuracy of over 97.4% and surpasses other state-of-the-art methods. Additionally, a mobile application has been developed for prompt diagnosis of metastases in early-stage cancer.

SENSORS (2022)

Review Oncology

Skin Cancer Classification With Deep Learning: A Systematic Review

Yinhao Wu et al.

Summary: This article provides a comprehensive overview of the latest deep learning-based algorithms for skin cancer classification. It discusses different types of dermatological images, publicly available datasets related to skin cancers, and successful applications of convolutional neural networks in skin cancer classification. The paper also highlights frontier problems such as data imbalance, data limitation, domain adaptation, model robustness, and model efficiency, along with corresponding solutions. The general development direction of these approaches is structured, lightweight, and multimodal. The findings are summarized in figures and tables for reader convenience.

FRONTIERS IN ONCOLOGY (2022)

Article Computer Science, Artificial Intelligence

An efficient bicubic interpolation implementation for real-time image processing using hybrid computing

Yubin Zhu et al.

Summary: This article proposes a hybrid architecture of fixed-point and stochastic computing for bicubic interpolation, which achieves high-quality image processing at a lower hardware cost. By ambiguously computing the low-weight bits, the proposed architecture reduces hardware resource consumption. Experimental results demonstrate significant resource reduction and improved image processing speed compared to existing architectures.

JOURNAL OF REAL-TIME IMAGE PROCESSING (2022)

Proceedings Paper Telecommunications

Variational Autoencoder Generative Adversarial Network for Synthetic Data Generation in Smart Home

Mina Razghandi et al.

Summary: This paper proposes a smart grid data generative model that can learn data distributions and generate synthetic data. Experimental results show that the proposed model outperforms the vanilla GAN network based on the analysis of differences between synthetic data and real data.

IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS (ICC 2022) (2022)

Article Multidisciplinary Sciences

Deep learning data augmentation for Raman spectroscopy cancer tissue classification

Man Wu et al.

Summary: The text discusses the role of Raman spectroscopy in cancer diagnosis and highlights the importance of data augmentation and generative adversarial networks in improving the accuracy of skin cancer tissue classification.

SCIENTIFIC REPORTS (2021)

Article Computer Science, Interdisciplinary Applications

Semi-supervise d GAN-base d Radiomics Model for Data Augmentation in Breast Ultrasound Mass Classification

Ting Pang et al.

Summary: This study developed a radiomics model based on a semi-supervised GAN method to perform data augmentation and classification of breast ultrasound images. By generating high-quality breast ultrasound images using generative adversarial network, we achieved more accurate breast mass classification results compared to other methods.

COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE (2021)

Article Chemistry, Analytical

Classification of Skin Disease Using Deep Learning Neural Networks with MobileNet V2 and LSTM

Parvathaneni Naga Srinivasu et al.

Summary: Deep learning models, specifically MobileNet V2 and LSTM, have been proposed for efficient classification of skin diseases, showing higher accuracy and effectiveness compared to other state-of-the-art models. The use of a grey-level co-occurrence matrix helps assess diseased growth progression, with the proposed method outperforming others with over 85% accuracy.

SENSORS (2021)

Article Engineering, Biomedical

CT synthesis from MRI using multi-cycle GAN for head-and-neck radiation therapy

Yanxia Liu et al.

Summary: The study introduced a novel framework called Multi-Cycle GAN for generating high-quality medical images, incorporating a new generator called Z-Net to improve accuracy of anatomical details. Extensive experiments demonstrated that Multi-Cycle GAN outperformed state-of-the-art CT synthesis methods and achieved significant improvements in multiple metrics.

COMPUTERIZED MEDICAL IMAGING AND GRAPHICS (2021)

Article Engineering, Biomedical

FA-GAN: Fused attentive generative adversarial networks for MRI image super-resolution

Mingfeng Jiang et al.

Summary: The paper introduces a framework called FA-GAN to generate super-resolution MR images from low-resolution ones, reducing scanning time effectively. By utilizing local fusion feature blocks, global feature fusion modules, and spectral normalization, important features of MR images are enhanced, leading to better reconstruction results compared to state-of-the-art methods.

COMPUTERIZED MEDICAL IMAGING AND GRAPHICS (2021)

Review Environmental Sciences

Skin Cancer Detection: A Review Using Deep Learning Techniques

Mehwish Dildar et al.

Summary: Skin cancer, caused by un-repaired DNA in skin cells, can be cured in initial stages through early detection. Researchers have developed various early detection techniques, including deep learning, to address the increasing rate of skin cancer cases. Analysis of lesion parameters can accurately diagnose skin cancer.

INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH (2021)

Proceedings Paper Computer Science, Artificial Intelligence

Synthetic Images Generation Using Conditional Generative Adversarial Network for Skin Cancer Classification

Ranpreet Kaur et al.

Summary: The paper introduces a method using conditional generative adversarial networks (CGAN) to generate high-resolution synthetic images to enhance the performance of skin cancer detection systems. The generator module aggregates information from all feature layers and successfully incorporates auxiliary information with image inputs, resulting in improved performance in skin cancer detection.

2021 IEEE REGION 10 CONFERENCE (TENCON 2021) (2021)

Article Engineering, Biomedical

Generative Adversarial Network Image Synthesis Method for Skin Lesion Generation and Classification

Freedom Mutepfe et al.

Summary: The study aims to develop an automatic skin cancer classification model using Deep Convolutional Generative Adversarial Network (DCGAN), which achieved an overall test accuracy of 93.5% after fine-tuning most parameters. This model provides spatial intelligence for cancer risk prediction in the future.

JOURNAL OF MEDICAL SIGNALS & SENSORS (2021)

Proceedings Paper Computer Science, Artificial Intelligence

The effect of image enhancement algorithms on convolutional neural networks

Jose A. Rodriguez-Rodriguez et al.

Summary: This study explores the impact of brightness and image contrast enhancement techniques on the performance of CNNs in classification tasks, analyzing various CNN architectures and image processing techniques through experiments.

2020 25TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR) (2021)

Article Computer Science, Theory & Methods

A survey on generative adversarial networks for imbalance problems in computer vision tasks

Vignesh Sampath et al.

Summary: This article discusses the importance of image and data acquisition, preprocessing, and pattern recognition in computer vision application development. Particularly, the occurrence of imbalance issues in complex real-world problems is inevitable. Research shows that techniques based on GANs are able to address these imbalances effectively and boost the performance of computer vision algorithms.

JOURNAL OF BIG DATA (2021)

Article Computer Science, Information Systems

Dermoscopy Image Classification Based on StyleGAN and DenseNet201

Chen Zhao et al.

Summary: This study proposes a new skin lesion image classification framework based on SLA-StyleGAN, which improves the application of deep learning in skin lesion classification through data augmentation methods, increasing classification accuracy. Experimental results show that the framework performs well on the ISIC2019 dataset, with a BMA of 93.64%.

IEEE ACCESS (2021)

Article Engineering, Biomedical

Deep neural network with generative adversarial networks pre-training for brain tumor classification based on MR images

Navid Ghassemi et al.

BIOMEDICAL SIGNAL PROCESSING AND CONTROL (2020)

Article Computer Science, Interdisciplinary Applications

A GAN-based image synthesis method for skin lesion classification

Zhiwei Qin et al.

COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE (2020)

Article Biology

DSNet: Automatic dermoscopic skin lesion segmentation

Md Kamrul Hasan et al.

COMPUTERS IN BIOLOGY AND MEDICINE (2020)

Article Oncology

A deep learning, image based approach for automated diagnosis for inflammatory skin diseases

Haijing Wu et al.

ANNALS OF TRANSLATIONAL MEDICINE (2020)

Review Computer Science, Artificial Intelligence

GANs for medical image analysis

Salome Kazeminia et al.

ARTIFICIAL INTELLIGENCE IN MEDICINE (2020)

Article Computer Science, Artificial Intelligence

Generative adversarial networks with decoder-encoder output noises

Guoqiang Zhong et al.

NEURAL NETWORKS (2020)

Article Computer Science, Information Systems

Image Enhancement for Tuberculosis Detection Using Deep Learning

Khairul Munadi et al.

IEEE ACCESS (2020)

Article Computer Science, Artificial Intelligence

Multi-Label classification of multi-modality skin lesion via hyper-connected convolutional neural network

Lei Bi et al.

PATTERN RECOGNITION (2020)

Article Computer Science, Artificial Intelligence

A machine learning approach to automatic detection of irregularity in skin lesion border using dermoscopic images

Abder-Rahman Ali et al.

PEERJ COMPUTER SCIENCE (2020)

Review Computer Science, Artificial Intelligence

Generative adversarial network in medical imaging: A review

Xin Yi et al.

MEDICAL IMAGE ANALYSIS (2019)

Proceedings Paper Engineering, Biomedical

Skin Lesion Classification Using GAN based Data Augmentation

Haroon Rashid et al.

2019 41ST ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC) (2019)

Article Computer Science, Theory & Methods

Deep convolutional neural network based medical image classification for disease diagnosis

Samir S. Yadav et al.

JOURNAL OF BIG DATA (2019)

Article Computer Science, Artificial Intelligence

A Parallel Multiclassification Algorithm for Big Data Using an Extreme Learning Machine

Mingxing Duan et al.

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2018)

Article Mathematical & Computational Biology

Skin Disease Recognition Method Based on Image Color and Texture Features

Li-sheng Wei et al.

COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE (2018)

Article Computer Science, Artificial Intelligence

GAN-based synthetic medical image augmentation for increased CNN performance in liver lesion classification

Maayan Frid-Adar et al.

NEUROCOMPUTING (2018)

Article Health Care Sciences & Services

Generative Adversarial Network for Medical Images (MI-GAN)

Talha Iqbal et al.

JOURNAL OF MEDICAL SYSTEMS (2018)

Article Multidisciplinary Sciences

Dermatologist-level classification of skin cancer with deep neural networks

Andre Esteva et al.

NATURE (2017)

Article Computer Science, Hardware & Architecture

ImageNet Classification with Deep Convolutional Neural Networks

Alex Krizhevsky et al.

COMMUNICATIONS OF THE ACM (2017)

Proceedings Paper Computer Science, Artificial Intelligence

Densely Connected Convolutional Networks

Gao Huang et al.

30TH IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2017) (2017)

Proceedings Paper Computer Science, Artificial Intelligence

DeLiGAN : Generative Adversarial Networks for Diverse and Limited Data

Swaminathan Gurumurthy et al.

30TH IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2017) (2017)

Review Computer Science, Artificial Intelligence

Learning from imbalanced data: open challenges and future directions

Bartosz Krawczyk

PROGRESS IN ARTIFICIAL INTELLIGENCE (2016)

Article Mathematical & Computational Biology

Medical Image Enhancement Based on Shear let Transform and Unsharp Masking

Ayiguli Wubuli et al.

JOURNAL OF MEDICAL IMAGING AND HEALTH INFORMATICS (2014)

Article Computer Science, Hardware & Architecture

High accuracy bicubic interpolation using image local features

Shuai Yuan et al.

IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES (2007)

Article Computer Science, Information Systems

Realization of the Contrast Limited Adaptive Histogram Equalization (CLAHE) for real-time image enhancement

AM Reza

JOURNAL OF VLSI SIGNAL PROCESSING SYSTEMS FOR SIGNAL IMAGE AND VIDEO TECHNOLOGY (2004)

Article Computer Science, Artificial Intelligence

Transform-based image enhancement algorithms with performance measure

SS Agaian et al.

IEEE TRANSACTIONS ON IMAGE PROCESSING (2001)

Article Computer Science, Artificial Intelligence

Image enhancement via adaptive unsharp masking

A Polesel et al.

IEEE TRANSACTIONS ON IMAGE PROCESSING (2000)