Deep Learning-Based System for Automatic Melanoma Detection

Melanoma is the deadliest form of skin cancer. Distinguishing melanoma lesions from non-melanoma lesions has however been a challenging task. Many Computer Aided Diagnosis and Detection Systems have been developed in the past for this task. They have been limited in performance due to the complex visual characteristics of the skin lesion images which consists of inhomogeneous features and fuzzy boundaries. In this paper, we propose a deep learning-based method that overcomes these limitations for automatic melanoma lesion detection and segmentation. An enhanced encoder-decoder network with encoder and decoder sub-networks connected through a series of skip pathways which brings the semantic level of the encoder feature maps closer to that of the decoder feature maps is proposed for efficient learning and feature extraction. The system employs multi-stage and multi-scale approach and utilizes softmax classifier for pixel-wise classification of melanoma lesions. We devise a new method called Lesion-classifier that performs the classification of skin lesions into melanoma and non-melanoma based on results derived from pixel-wise classification. Our experiments on two well-established public benchmark skin lesion datasets, International Symposium on Biomedical Imaging(ISBI)2017 and Hospital Pedro Hispano (PH2), demonstrate that our method is more effective than some state-of-the-art methods. We achieved accuracy and dice coefficient of 95% and 92% on ISIC 2017 dataset and accuracy and dice coefficient of 95% and 93% on PH2 datasets.

[1]  N. Ramanujam.  Fluorescence spectroscopy of neoplastic and non-neoplastic tissues. , 2000, Neoplasia.

[2]  Huadong Guo,et al.  A Hierarchical Multiscale Super-Pixel-Based Classification Method for Extracting Urban Impervious Surface Using Deep Residual Network From WorldView-2 and LiDAR Data , 2019, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[3]  Christoph Rasche.  Melanoma Recognition with an Ensemble of Techniques for Segmentation and a Structural Analysis for Classification , 2018, ArXiv.

[4]  Qaisar Abbas,et al.  Pattern classification of dermoscopy images: A perceptually uniform model , 2013, Pattern Recognit..

[5]  Chithra,et al.  A Review on Segmentation Techniques in Skin Lesion Images , 2016 .

[6]  Randy H. Moss,et al.  A methodological approach to the classification of dermoscopy images , 2007, Comput. Medical Imaging Graph..

[7]  R Marchesini,et al.  The ABCD system of melanoma detection , 1999, Cancer.

[8]  A. McMahon,et al.  The use of the dermatoscope to identify early melanoma using the three‐colour test , 2002, The British journal of dermatology.

[9]  Brian C. Wilson,et al.  Fluorescence and Spectral Imaging , 2007, TheScientificWorldJournal.

[10]  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).

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

[12]  Allan C Halpern,et al.  Current and emerging technologies in melanoma diagnosis: the state of the art. , 2009, Clinics in dermatology.

[13]  A. Enk,et al.  Systematic outperformance of 112 dermatologists in multiclass skin cancer image classification by convolutional neural networks. , 2019, European journal of cancer.

[14]  Ruqaiya Khanam,et al.  An efficient 3D color-texture feature and neural network technique for melanoma detection , 2019, Informatics in Medicine Unlocked.

[15]  David Dagan Feng,et al.  Automatic melanoma detection via multi-scale lesion-biased representation and joint reverse classification , 2016, 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI).

[16]  Jong-Seo Lee,et al.  Malignant Skin Tumor Misdiagnosed as a Benign Skin Lesion , 2016, Archives of craniofacial surgery.

[17]  Victoria McGovern.  Digital diagnosis: new tool for detecting skin cancer. , 2003, Environmental health perspectives.

[18]  J. Stockman,et al.  Distinct Sets of Genetic Alterations in Melanoma , 2007 .

[19]  Serestina Viriri,et al.  An Enhanced Deep Learning Framework for Skin Lesions Segmentation , 2019, ICCCI.

[20]  Jorge S. Marques,et al.  Bag-of-Features Classification Model for the Diagnose of Melanoma in Dermoscopy Images Using Color and Texture Descriptors , 2013, ICIAR.

[21]  Jianwen Luo,et al.  Direct Reconstruction of Ultrasound Elastography Using an End-to-End Deep Neural Network , 2018, MICCAI.

[22]  Dhanesh Ramachandram,et al.  LesionSeg: Semantic segmentation of skin lesions using Deep Convolutional Neural Network , 2017, ArXiv.

[23]  Sébastien Ourselin,et al.  Generalised Dice overlap as a deep learning loss function for highly unbalanced segmentations , 2017, DLMIA/ML-CDS@MICCAI.

[24]  Mun-Taek Choi,et al.  Skin lesion segmentation in dermoscopy images via deep full resolution convolutional networks , 2018, Comput. Methods Programs Biomed..

[25]  S Sivaraj,et al.  Detecting anomalous growth of skin lesion using threshold-based segmentation algorithm and Fuzzy K-Nearest Neighbor classifier. , 2020, Journal of cancer research and therapeutics.

[26]  Russell C. Hardie,et al.  Skin Lesion Segmentation and Classification for ISIC 2018 Using Traditional Classifiers with Hand-Crafted Features , 2018, ArXiv.

[27]  Yading Yuan,et al.  Improving Dermoscopic Image Segmentation With Enhanced Convolutional-Deconvolutional Networks , 2017, IEEE Journal of Biomedical and Health Informatics.

[28]  Amna Waheed,et al.  An efficient machine learning approach for the detection of melanoma using dermoscopic images , 2017, 2017 International Conference on Communication, Computing and Digital Systems (C-CODE).

[29]  Ashfaq A Marghoob,et al.  Instruments and new technologies for the in vivo diagnosis of melanoma. , 2003, Journal of the American Academy of Dermatology.

[30]  Xin Wang,et al.  Invasive Cancer Detection Utilizing Compressed Convolutional Neural Network and Transfer Learning , 2018, MICCAI.

[31]  Dorra Sellami,et al.  Deep Neural Network for Fuzzy Automatic Melanoma Diagnosis , 2019, VISIGRAPP.

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

[33]  Lucia Maddalena,et al.  Segmenting Dermoscopic Images , 2017, ArXiv.

[34]  P. Carli,et al.  Dermoscopy and Early Diagnosis of Melanoma: The Light and the Dark , 2001 .

[35]  Daniele Nardi,et al.  Skin lesion image segmentation using Delaunay Triangulation for melanoma detection , 2016, Comput. Medical Imaging Graph..

[36]  Harold S Rabinovitz,et al.  Dermoscopy of pigmented skin lesions. , 2005, Journal of the American Academy of Dermatology.

[37]  Manu Goyal,et al.  Multi-class Semantic Segmentation of Skin Lesions via Fully Convolutional Networks , 2017, BIOINFORMATICS.

[38]  Cristina Kurachi,et al.  Optical Imaging as Auxiliary Tool in Skin Cancer Diagnosis , 2011 .

[39]  Michael B Atkins,et al.  Management of cutaneous melanoma. , 2004, The New England journal of medicine.

[40]  Shaoting Zhang,et al.  Recognizing End-Diastole and End-Systole Frames via Deep Temporal Regression Network , 2016, MICCAI.

[41]  Serestina Viriri,et al.  Skin Lesion Images Segmentation: A Survey of the State-of-the-Art , 2018, MIKE.

[42]  Noel C. F. Codella,et al.  Skin Lesion Analysis Toward Melanoma Detection 2018: A Challenge Hosted by the International Skin Imaging Collaboration (ISIC) , 2019, ArXiv.

[43]  Germán Capdehourat,et al.  Toward a combined tool to assist dermatologists in melanoma detection from dermoscopic images of pigmented skin lesions , 2011, Pattern Recognit. Lett..

[44]  Muhammad Imran Razzak,et al.  Deep Learning for Medical Image Processing: Overview, Challenges and Future , 2017, ArXiv.

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

[46]  Khalid M. Hosny,et al.  Skin Cancer Classification using Deep Learning and Transfer Learning , 2018, 2018 9th Cairo International Biomedical Engineering Conference (CIBEC).

[47]  Randy H. Moss,et al.  Automatic detection of blue-white veil and related structures in dermoscopy images , 2008, Comput. Medical Imaging Graph..

[48]  Gerald Schaefer,et al.  An ensemble classification approach for melanoma diagnosis , 2014, Memetic Computing.

[49]  Josep Malvehy,et al.  Diagnosis and treatment of melanoma. European consensus-based interdisciplinary guideline - Update 2016. , 2012, European journal of cancer.

[50]  Tianfu Wang,et al.  Dense deconvolution net: Multi path fusion and dense deconvolution for high resolution skin lesion segmentation , 2018, Technology and health care : official journal of the European Society for Engineering and Medicine.

[51]  D. Pinkel,et al.  Somatic activation of KIT in distinct subtypes of melanoma. , 2006, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.