Automatic skin cancer detection in dermoscopy images by combining convolutional neural networks and texture features

[1]  Newton Spolaôr,et al.  Dermoscopic assisted diagnosis in melanoma: Reviewing results, optimizing methodologies and quantifying empirical guidelines , 2018, Knowl. Based Syst..

[2]  Yuan Zhang,et al.  Classification of melanoma based on feature similarity measurement for codebook learning in the bag-of-features model , 2019, Biomed. Signal Process. Control..

[3]  Jon Atli Benediktsson,et al.  Automatic Design of Convolutional Neural Network for Hyperspectral Image Classification , 2019, IEEE Transactions on Geoscience and Remote Sensing.

[4]  David A. Landgrebe,et al.  A survey of decision tree classifier methodology , 1991, IEEE Trans. Syst. Man Cybern..

[5]  Sergey Ioffe,et al.  Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift , 2015, ICML.

[6]  Hao Chen,et al.  Automated Melanoma Recognition in Dermoscopy Images via Very Deep Residual Networks , 2017, IEEE Transactions on Medical Imaging.

[7]  Nauman Aslam,et al.  Diagnosis and monitoring of Alzheimer's patients using classical and deep learning techniques , 2019, Expert Syst. Appl..

[8]  T Lee,et al.  Dullrazor®: A software approach to hair removal from images , 1997, Comput. Biol. Medicine.

[9]  Yidi Wang,et al.  A Training Data Set Cleaning Method by Classification Ability Ranking for the $k$ -Nearest Neighbor Classifier , 2020, IEEE Transactions on Neural Networks and Learning Systems.

[10]  U. Rajendra Acharya,et al.  Automated detection of diabetic subject using pre-trained 2D-CNN models with frequency spectrum images extracted from heart rate signals , 2019, Comput. Biol. Medicine.

[11]  Pedro M. Ferreira,et al.  PH2 - A dermoscopic image database for research and benchmarking , 2013, 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[12]  Diego Klabjan,et al.  L1-norm Kernel PCA , 2017, ArXiv.

[13]  Sharath Pankanti,et al.  Deep learning ensembles for melanoma recognition in dermoscopy images , 2016, IBM J. Res. Dev..

[14]  E. de Vries,et al.  Population‐based incidence and melanoma‐specific survival of cutaneous malignant melanoma in a Colombian population 2000–2009 , 2018, International journal of dermatology.

[15]  I. Zalaudek,et al.  Dermoscopy of basal cell carcinoma , 2018, Clinical and experimental dermatology.

[16]  Johan A. K. Suykens,et al.  Least Squares Support Vector Machine Classifiers , 1999, Neural Processing Letters.

[17]  Zhiguo Jiang,et al.  Skin lesion segmentation using high-resolution convolutional neural network , 2019, Comput. Methods Programs Biomed..

[18]  Euishin Edmund Kim.  Artificial Intelligence and Computer-aided Diagnosis in Medicine. , 2020, Current medical imaging reviews.

[19]  Dharmendra Singh Rajput,et al.  Impact of Gradient Ascent and Boosting Algorithm in Classification , 2018 .

[20]  Yi Xiong,et al.  Prediction of Effective Drug Combinations by an Improved Naïve Bayesian Algorithm , 2018, International journal of molecular sciences.

[21]  Yibin Li,et al.  Facial Expression Recognition with Fusion Features Extracted from Salient Facial Areas , 2017, Sensors.

[22]  Yang Li,et al.  Melanoma Classification on Dermoscopy Images Using a Neural Network Ensemble Model , 2017, IEEE Transactions on Medical Imaging.

[23]  Paul Babyn,et al.  Unsupervised and semi-supervised learning with Categorical Generative Adversarial Networks assisted by Wasserstein distance for dermoscopy image Classification , 2018, ArXiv.

[24]  Tanzila Saba,et al.  Region Extraction and Classification of Skin Cancer: A Heterogeneous framework of Deep CNN Features Fusion and Reduction , 2019, Journal of Medical Systems.

[25]  Paulo J. G. Lisboa,et al.  The Use of Artificial Neural Networks in Decision Support in Cancer: a Systematic Review , 2005 .

[26]  Babak Mohammadzadeh Asl,et al.  Melanoma recognition in dermoscopy images using lesion's peripheral region information , 2018, Comput. Methods Programs Biomed..

[27]  Suneeta Agarwal,et al.  Image classification using SURF and bag of LBP features constructed by clustering with fixed centers , 2018, Multimedia Tools and Applications.

[28]  Nezam Mahdavi-Amiri,et al.  Kernel sparse representation based model for skin lesions segmentation and classification , 2019, Comput. Methods Programs Biomed..

[29]  Dong-Hyun Kim,et al.  Multiple skin lesions diagnostics via integrated deep convolutional networks for segmentation and classification , 2020, Comput. Methods Programs Biomed..

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

[31]  SchmidhuberJürgen.  Deep learning in neural networks , 2015 .

[32]  P. Tschandl,et al.  Accuracy of Computer-Aided Diagnosis of Melanoma: A Meta-analysis. , 2019, JAMA dermatology.

[33]  Robert M. Haralick,et al.  Textural Features for Image Classification , 1973, IEEE Trans. Syst. Man Cybern..

[34]  Navid Razmjooy,et al.  Computer-Aided Diagnosis of Skin Cancer: A Review. , 2020, Current medical imaging reviews.

[35]  Yichuan Tang,et al.  Deep Learning using Linear Support Vector Machines , 2013, 1306.0239.

[36]  Perry Xiao,et al.  In vivo skin capacitive imaging analysis by using grey level co-occurrence matrix (GLCM). , 2014, International journal of pharmaceutics.

[37]  Michael Unser,et al.  Convolutional Neural Networks for Inverse Problems in Imaging: A Review , 2017, IEEE Signal Processing Magazine.

[38]  Debangshu Dey,et al.  Integration of morphological preprocessing and fractal based feature extraction with recursive feature elimination for skin lesion types classification , 2019, Comput. Methods Programs Biomed..

[39]  M. Jameson,et al.  Skin lesions suspicious for melanoma: New Zealand excision margin guidelines in practice. , 2018, Journal of primary health care.

[40]  Jürgen Schmidhuber,et al.  Deep learning in neural networks: An overview , 2014, Neural Networks.

[41]  Xuelong Li,et al.  Query-aware sparse coding for web multi-video summarization , 2019, Inf. Sci..

[42]  Qiang Tian,et al.  Radiomics strategy for glioma grading using texture features from multiparametric MRI , 2018, Journal of magnetic resonance imaging : JMRI.

[43]  Md Ashraful Alam Milton.  Automated Skin Lesion Classification Using Ensemble of Deep Neural Networks in ISIC 2018: Skin Lesion Analysis Towards Melanoma Detection Challenge , 2019, ArXiv.

[44]  H. Soyer,et al.  Early detection of melanoma: a consensus report from the Australian Skin and Skin Cancer Research Centre Melanoma Screening Summit , 2020, Australian and New Zealand journal of public health.