Pigmented Skin Lesions Classification using Convolutional Neural Networks

In this paper we present an architecture for classification of pigmented skin lesions from dermatoscopic images. The architecture is using image pre-processing for natural hair removal and image segmentation for extraction of the skin lesion area. The segmented images were processed by a convolutional neural network classifier. The training process was done by using the Keras and TensorFlow python packets with CUDA supported. The best performance was achieved by a convolutional neural network architecture with three convolution layers and the classification accuracy was equal to 76.83%.

[1]  Ali Serener,et al.  Deep Learning for Two-Step Classification of Malignant Pigmented Skin Lesions , 2018, 2018 14th Symposium on Neural Networks and Applications (NEUREL).

[2]  Ovidiu Daescu,et al.  Deep learning for skin lesion segmentation , 2017, 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM).

[3]  Yasser Baleghi,et al.  Skin lesion images classification using new color pigmented boundary descriptors , 2017, 2017 3rd International Conference on Pattern Recognition and Image Analysis (IPRIA).

[4]  Xiaoxiao Li,et al.  Skin Lesion Classification Via Combining Deep Learning Features and Clinical Criteria Representations , 2018 .

[5]  Mohammad H. Jafari,et al.  Skin lesion segmentation in clinical images using deep learning , 2016, 2016 23rd International Conference on Pattern Recognition (ICPR).

[6]  Julie Ann A. Salido,et al.  HAIR ARTIFACT REMOVAL AND SKIN LESION SEGMENTATION OF DERMOSCOPY IMAGES , 2018 .

[7]  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).

[8]  Germán Capdehourat,et al.  Pigmented Skin Lesions Classification Using Dermatoscopic Images , 2009, CIARP.

[9]  Mai S. Mabrouk,et al.  Automated Imaging System for Pigmented Skin Lesion Diagnosis , 2016 .

[10]  Martin Halicek,et al.  SKIN LESION CLASSIFICATION : TRANSFORMATION-BASED APPROACH TO CONVOLUTIONAL NEURAL NETWORKS , 2017 .

[11]  Gerard de Haan,et al.  Automatic imaging sysem with decision support for inspection of pigmented skin lesions and melanoma diagnosis. , 2009 .

[12]  Pietro Rubegni,et al.  Automated diagnosis of pigmented skin lesions , 2002, International journal of cancer.

[13]  Tony F. Chan,et al.  Active contours without edges , 2001, IEEE Trans. Image Process..

[14]  Zeeshan Abbas,et al.  Classification of Skin Lesion by Interference of Segmentation and Convolotion Neural Network , 2018, 2018 2nd International Conference on Engineering Innovation (ICEI).

[15]  Ghassan Hamarneh,et al.  Deep features to classify skin lesions , 2016, 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI).

[16]  D. Whiteman,et al.  Diagnostic accuracy in skin cancer clinics: the Australian experience , 2006, International journal of dermatology.

[17]  W. Stolz,et al.  The ABCD rule of dermatoscopy. High prospective value in the diagnosis of doubtful melanocytic skin lesions. , 1994, Journal of the American Academy of Dermatology.

[18]  Achmad Benny Mutiara,et al.  Classification of Melanoma Skin Cancer using Convolutional Neural Network , 2019, International Journal of Advanced Computer Science and Applications.

[19]  Josiane Zerubia,et al.  Classification of skin hyper-pigmentation lesions with multi-spectral images , 2012 .

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

[21]  W. Chang,et al.  Computer-Aided Diagnosis of Skin Lesions Using Conventional Digital Photography: A Reliability and Feasibility Study , 2013, PloS one.

[22]  M. Binder,et al.  Epiluminescence microscopy. A useful tool for the diagnosis of pigmented skin lesions for formally trained dermatologists. , 1995, Archives of dermatology.

[23]  A. Jerant,et al.  Early detection and treatment of skin cancer. , 2000, American family physician.

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

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