Deep Learning for Melanoma Detection with Testing Time Data Augmentation

[1]  Van-Dung Hoang,et al.  Hybrid Deep Learning and Data Augmentation for Disease Candidate Extraction , 2020, IW-FCV.

[2]  Mark Sandler,et al.  MobileNetV2: Inverted Residuals and Linear Bottlenecks , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[4]  Yuri Gordienko,et al.  Batch Size Influence on Performance of Graphic and Tensor Processing Units during Training and Inference Phases , 2018, Advances in Computer Science for Engineering and Education II.

[5]  Steen Moeller,et al.  Deep-Learning Methods for Parallel Magnetic Resonance Imaging Reconstruction: A Survey of the Current Approaches, Trends, and Issues , 2020, IEEE Signal Processing Magazine.

[6]  Dumitru Erhan,et al.  Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[7]  Daniel Ajisafe,et al.  Early Skin Cancer Detection Using Deep Convolutional Neural Networks on Mobile Smartphone , 2020, International Journal of Information Engineering and Electronic Business.

[8]  Hiroshi Koga,et al.  Performance Improvement of Automated Melanoma Diagnosis System by Data Augmentation , 2020, Advanced Biomedical Engineering.

[9]  U. Rajendra Acharya,et al.  Deep learning for healthcare applications based on physiological signals: A review , 2018, Comput. Methods Programs Biomed..

[10]  François Chollet,et al.  Xception: Deep Learning with Depthwise Separable Convolutions , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[11]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[12]  Adel Khelifi,et al.  Malignant Melanoma Classification Using Deep Learning: Datasets, Performance Measurements, Challenges and Opportunities , 2020, IEEE Access.

[13]  Kilian Q. Weinberger,et al.  Densely Connected Convolutional Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[14]  Sudhanshu Kulshrestha,et al.  Machine Learning Approaches for Cancer Detection , 2018 .

[15]  Yuriy Kochura,et al.  Scaling Analysis of Specialized Tensor Processing Architectures for Deep Learning Models , 2019, Deep Learning: Concepts and Architectures.

[16]  Khalid M. Hosny,et al.  Skin melanoma classification using ROI and data augmentation with deep convolutional neural networks , 2020, Multimedia Tools and Applications.

[17]  K. Skala,et al.  Augmented Coaching Ecosystem for Non-obtrusive Adaptive Personalized Elderly Care on the basis of Cloud-Fog-Dew computing paradigm , 2017, 2017 40th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO).

[18]  Quoc V. Le,et al.  EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks , 2019, ICML.

[19]  Andrew Y. Ng,et al.  CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning , 2017, ArXiv.

[20]  Serestina Viriri,et al.  Deep learning techniques for skin lesion analysis and melanoma cancer detection: a survey of state-of-the-art , 2020, Artificial Intelligence Review.

[21]  Peng Gang,et al.  Dimensionality reduction in deep learning for chest X-ray analysis of lung cancer , 2018, 2018 Tenth International Conference on Advanced Computational Intelligence (ICACI).

[22]  Yudong Zhang,et al.  A Review of Deep Learning on Medical Image Analysis , 2020, Mobile Networks and Applications.

[23]  Peyman Hosseinzadeh Kassani,et al.  A comparative study of deep learning architectures on melanoma detection. , 2019, Tissue & cell.

[24]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[25]  Abbas H. Hassin Alasadi,et al.  Early Detection and Classification of Melanoma Skin Cancer , 2016 .

[26]  Sergey Ioffe,et al.  Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning , 2016, AAAI.

[27]  Andre Esteva,et al.  A guide to deep learning in healthcare , 2019, Nature Medicine.

[28]  LinLin Shen,et al.  Skin Lesion Analysis towards Melanoma Detection Using Deep Learning Network , 2017, Sensors.

[29]  Peng Gang,et al.  Effect of Data Augmentation and Lung Mask Segmentation for Automated Chest Radiograph Interpretation of Some Lung Diseases , 2019, ICONIP.

[30]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[31]  Wei Zeng,et al.  Deep Learning with Lung Segmentation and Bone Shadow Exclusion Techniques for Chest X-Ray Analysis of Lung Cancer , 2017, ArXiv.

[32]  Eduardo Valle,et al.  Knowledge transfer for melanoma screening with deep learning , 2017, 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017).

[33]  Keyan Cao,et al.  An Overview on Edge Computing Research , 2020, IEEE Access.

[34]  Niladri B. Puhan,et al.  Recent Deep Learning Methods for Melanoma Detection: A Review , 2018, ICMC.

[35]  Xiaoyu Cui,et al.  Assessing the effectiveness of artificial intelligence methods for melanoma:A retrospective review. , 2019, Journal of the American Academy of Dermatology.

[36]  Sergey Ioffe,et al.  Rethinking the Inception Architecture for Computer Vision , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[37]  John Collomosse,et al.  Deep learning with wearable based heart rate variability for prediction of mental and general health , 2020, J. Biomed. Informatics.

[38]  Wei Zeng,et al.  Chest X-Ray Analysis of Tuberculosis by Deep Learning with Segmentation and Augmentation , 2018, 2018 IEEE 38th International Conference on Electronics and Nanotechnology (ELNANO).