DePicT Melanoma Deep-CLASS: a deep convolutional neural networks approach to classify skin lesion images

Background Melanoma results in the vast majority of skin cancer deaths during the last decades, even though this disease accounts for only one percent of all skin cancers’ instances. The survival rates of melanoma from early to terminal stages is more than fifty percent. Therefore, having the right information at the right time by early detection with monitoring skin lesions to find potential problems is essential to surviving this type of cancer. Results An approach to classify skin lesions using deep learning for early detection of melanoma in a case-based reasoning (CBR) system is proposed. This approach has been employed for retrieving new input images from the case base of the proposed system DePicT Melanoma Deep-CLASS to support users with more accurate recommendations relevant to their requested problem (e.g., image of affected area). The efficiency of our system has been verified by utilizing the ISIC Archive dataset in analysis of skin lesion classification as a benign and malignant melanoma. The kernel of DePicT Melanoma Deep-CLASS is built upon a convolutional neural network (CNN) composed of sixteen layers (excluding input and ouput layers), which can be recursively trained and learned. Our approach depicts an improved performance and accuracy in testing on the ISIC Archive dataset. Conclusions Our methodology derived from a deep CNN, generates case representations for our case base to use in the retrieval process. Integration of this approach to DePicT Melanoma CLASS, significantly improving the efficiency of its image classification and the quality of the recommendation part of the system. The proposed method has been tested and validated on 1796 dermoscopy images. Analyzed results indicate that it is efficient on malignancy detection.

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

[2]  Yoshua Bengio,et al.  Understanding the difficulty of training deep feedforward neural networks , 2010, AISTATS.

[3]  Jon Y. Hardeberg,et al.  Enhancing dermoscopy images to improve melanoma detection , 2018, 2018 Colour and Visual Computing Symposium (CVCS).

[4]  Harald Kittler,et al.  Descriptor : The HAM 10000 dataset , a large collection of multi-source dermatoscopic images of common pigmented skin lesions , 2018 .

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

[6]  Sebastian Thrun,et al.  Dermatologist-level classification of skin cancer with deep neural networks , 2017, Nature.

[7]  Brian R. Gastman,et al.  NCCN Guidelines® insights: Melanoma, version 3.2016: Featured updates to the NCCN Guidelines , 2016 .

[8]  Iván González-Díaz,et al.  Incorporating the Knowledge of Dermatologists to Convolutional Neural Networks for the Diagnosis of Skin Lesions , 2017, ArXiv.

[9]  Eric Vander Putten,et al.  Deep residual neural networks for automated Basal Cell Carcinoma detection , 2018, 2018 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI).

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

[11]  Nitish Srivastava,et al.  Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..

[12]  Mikko Haavisto.  Pretraining Convolutional Neural Networks for Visual Recognition , 2016 .

[13]  Noel C. F. Codella,et al.  Skin lesion analysis toward melanoma detection: A challenge at the 2017 International symposium on biomedical imaging (ISBI), hosted by the international skin imaging collaboration (ISIC) , 2016, 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018).

[14]  Sara Nasiri,et al.  Detect and Predict Melanoma Utilizing TCBR and Classification of Skin Lesions in a Learning Assistant System , 2018, IWBBIO.

[15]  John Collins,et al.  A cascade classifier for diagnosis of melanoma in clinical images , 2014, 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[16]  Sejung Yang,et al.  Correction: Acral melanoma detection using a convolutional neural network for dermoscopy images , 2018, PloS one.

[17]  Sara Nasiri,et al.  Dynamic knowledge assets management to interactive problem solving and sustained learning: a collaborative CBR system in chronic and palliative care , 2018 .

[18]  U. G. Dailey.  Cancer,Facts and Figures about. , 2022, Journal of the National Medical Association.

[19]  Hilde van der Togt,et al.  Publisher's Note , 2003, J. Netw. Comput. Appl..

[20]  A. Rustgi,et al.  The genetics of hereditary colon cancer. , 2007, Genes & development.

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

[22]  Iv'an Gonz'alez D'iaz.  Incorporating the Knowledge of Dermatologists to Convolutional Neural Networks for the Diagnosis of Skin Lesions , 2017 .

[23]  D. S. Guru,et al.  Segmentation and Classification of Skin Lesions for Disease Diagnosis , 2016, ArXiv.

[24]  Akio Kimura,et al.  A SVM-based diagnosis of melanoma using only useful image features , 2018, 2018 International Workshop on Advanced Image Technology (IWAIT).

[25]  Trevor Darrell,et al.  Caffe: Convolutional Architecture for Fast Feature Embedding , 2014, ACM Multimedia.

[26]  S. W. Lee,et al.  Acral melanoma detection using a convolutional neural network for dermoscopy images , 2018, PloS one.

[27]  Sara Nasiri,et al.  Improving CBR adaptation for recommendation of associated references in a knowledge-based learning assistant system , 2017, Neurocomputing.

[28]  Cordelia Schmid,et al.  Transformation Pursuit for Image Classification , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[29]  A. Suruliandi,et al.  Melanoma Detection in Dermoscopic Images using Global and Local Feature Extraction , 2017, MUE 2017.

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

[31]  Benjamin Barnes,et al.  Bericht zum Krebsgeschehen in Deutschland 2016 , 2017 .

[32]  Arezoo Zakeri,et al.  Automatic Diagnosis of Melanoma Using Log-Linearized Gaussian Mixture Network , 2017, 2017 24th National and 2nd International Iranian Conference on Biomedical Engineering (ICBME).

[33]  Tara N. Sainath,et al.  Improving deep neural networks for LVCSR using rectified linear units and dropout , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.

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

[35]  Hiroshi Koga,et al.  Image Classification of Melanoma, Nevus and Seborrheic Keratosis by Deep Neural Network Ensemble , 2017, ArXiv.

[36]  Merrick I Ross,et al.  NCCN Guidelines Insights: Melanoma, Version 3.2016. , 2016, Journal of the National Comprehensive Cancer Network : JNCCN.

[37]  Luis Perez,et al.  The Effectiveness of Data Augmentation in Image Classification using Deep Learning , 2017, ArXiv.

[38]  Sven Behnke,et al.  Evaluation of Pooling Operations in Convolutional Architectures for Object Recognition , 2010, ICANN.

[39]  Eduardo Valle,et al.  RECOD Titans at ISIC Challenge 2017 , 2017, ArXiv.