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Computer-aided diagnosis (CAD) system based on multi-layer feature fusion network for skin lesion recognition in dermoscopy images

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A Correction to this article was published on 04 May 2020

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Abstract

Skin lesion recognition is one of the most important tasks in dermoscopic image analysis. Current Convolutional Neural Network (CNN) algorithms based recognition methods tend to become a standard methodology to fix a large array of Computer-Aided Diagnosis (CAD) and interpretation problems. Besides significant practical and theoretical improvements in their architecture, their effectiveness is built on the existence of the flexible pre-trained models which generalize well to novel tasks and handle the problem of having small set of dermoscopic data. However, existing works pay little attention to exploring the benefits of hierarchical multi-feature fusion for classifying the skin lesions in digital dermoscopic images. Practically, it has been found that integrating multi-layer features has significant potential for improving performance of any pattern recognition task. In this paper, we developed a robust CAD system based on transfer learning and multi-layer feature fusion network to diagnose complex skin diseases. It is a convenient approach in terms of overfitting prevention, convergence speed and high morphological feature similarity processing. Our research focuses exclusively on obtaining optimal performance with addressing the various gaps in the skin pattern recognition area. For validation and comparison purposes, the proposed approach was evaluated on publicly dermoscopic dataset, and achieved the high recognition precision compared with fully trained CNN models, fine-tuning process, single CNN model and other related works. Therefore, the study demonstrates that our proposed approach can dramatically improve the performance of CAD systems which are based on the conventional recognition and classification algorithms for skin lesion recognition in dermoscopic data.

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  • 04 May 2020

    The Eqs. 5 and 9 in the original publication of this article contained an error. The original article has been corrected.

References

  1. Abd-Ellah M, Awad A, Khalaf A, Hamed H (2018) Two-phase multi-model automatic brain tumour diagnosis system from magnetic resonance images using convolutional neural networks. EURASIP Journal on Image and Video Processing 2018(97):1–10. https://doi.org/10.1186/s13640-018-0332-4

    Google Scholar 

  2. Agrawal P, Girshick R, Malik J (2014) Analyzing the performance of multilayer neural networks for object recognition. In: Computer vision-ECCV 2014 329-344. https://doi.org/10.1007/978-3-319-10584-0_22

  3. Akcay S, Kundegorski M, Devereux M, Breckon T (2016) Transfer learning using convolutional neural networks for object classification within X-ray baggage security imagery. In: 2016 IEEE international conference on image processing (ICIP). https://doi.org/10.1109/icip.2016.7532519

  4. Anic G, Sondak V, Messina J, Fenske N, Zager J, Cherpelis B, Lee J, Fulp W, Epling-Burnette P, Park J, Rollison D (2013) Telomere length and risk of melanoma, squamous cell carcinoma, and basal cell carcinoma. Cancer Epidemiol 37(4):434–439. https://doi.org/10.1016/j.canep.2013.02.010

    Google Scholar 

  5. Antropova N, Huynh B, Giger M (2017) A deep feature fusion methodology for breast cancer diagnosis demonstrated on three imaging modality datasets. Med Phys 44(10):5162–5171. https://doi.org/10.1002/mp.12453

    Google Scholar 

  6. Anwar S, Majid M, Qayyum A, Awais M, Alnowami M, Khan M (2018) Medical image analysis using convolutional neural networks: A review. J Med Syst 42(11):1–13. https://doi.org/10.1007/s10916-018-1088-1

    Google Scholar 

  7. Aubreville M, Knipfer C, Oetter N, Jaremenko C, Rodner E, Denzler J, Bohr C, Neumann H, Stelzle F, Maier A (2017) Automatic classification of cancerous tissue in Laserendomicroscopy images of the oral cavity using deep learning. Sci Rep 7(1):1–10. https://doi.org/10.1038/s41598-017-12320-8

    Google Scholar 

  8. Azizpour H, Razavian A, Sullivan J, Maki A, Carlsson S (2015) From generic to specific deep representations for visual recognition. In: 2015 IEEE conference on computer vision and pattern recognition workshops (CVPRW). https://doi.org/10.1109/cvprw.2015.7301270

  9. Bakkouri I, Afdel K (2017) Breast tumor classification based on deep convolutional neural networks. In: 2017 international conference on advanced technologies for signal and image processing (ATSIP). https://doi.org/10.1109/atsip.2017.8075562

  10. Bakkouri I, Afdel K (2018) Convolutional neural-adaptive networks for melanoma recognition. In: International conference on image and signal processing 453-460. https://doi.org/10.1007/978-3-319-94211-7_49

  11. Bakkouri I, Afdel K (2018) Multi-scale CNN based on region proposals for efficient breast abnormality recognition. Multimed Tools Appl 78(10):12939–12960. https://doi.org/10.1007/s11042-018-6267-z

    Google Scholar 

  12. Baldi P, Sadowski P (2014) The dropout learning algorithm. Artif Intell 210:78–122. https://doi.org/10.1016/j.artint.2014.02.004

    MathSciNet  MATH  Google Scholar 

  13. Banerjee I, Crawley A, Bhethanabotla M, Daldrup-Link H, Rubin D (2018) Transfer learning on fused multiparametric MR images for classifying histopathological subtypes of rhabdomyosarcoma. Comput Med Imaging Graph 65:167–175. https://doi.org/10.1016/j.compmedimag.2017.05.002

    Google Scholar 

  14. Byra M, Styczynski G, Szmigielski C, Kalinowski P, Michalowski L, Paluszkiewicz R, Ziarkiewicz-Wroblewska B, Zieniewicz K, Sobieraj P, Nowicki A (2018) Transfer learning with deep convolutional neural network for liver steatosis assessment in ultrasound images. Int J Comput Assist Radiol Surg 13(12):1895–1903. https://doi.org/10.1007/s11548-018-1843-2

    Google Scholar 

  15. Castrejon L, Aytar Y, Vondrick C, Pirsiavash H, Torralba A (2016) Learning aligned cross-modal representations from weakly aligned data. In: 2016 IEEE conference on computer vision and pattern recognition (CVPR). https://doi.org/10.1109/cvpr.2016.321

  16. Chen W, Samuelson F (2014) The average receiver operating characteristic curve in multireader multicase imaging studies. Br J Radiol 87(1040):1–8. https://doi.org/10.1259/bjr.20140016

    Google Scholar 

  17. Chen Y, Xie H, Shin H (2018) Multi-layer fusion techniques using a CNN for multispectral pedestrian detection. IET Comput Vis 12(8):1179–1187. https://doi.org/10.1049/iet-cvi.2018.5315

    Google Scholar 

  18. Chougrad H, Zouaki H, Alheyane O (2018) Deep convolutional neural networks for breast cancer screening. Comput Methods Programs Biomed 157:19–30. https://doi.org/10.1016/j.cmpb.2018.01.011

    Google Scholar 

  19. Chu B, Madhavan V, Beijbom O, Hoffman J, Darrell T (2016) Best practices for fine-tuning visual classifiers to new domains. In: European conference on computer vision 9915:435-442. https://doi.org/10.1007/978-3-319-49409-8_34

  20. Chu J, Guo Z, Leng L (2018) Object detection based on multi-layer convolution feature fusion and online hard example mining. IEEE Access 6:19959–19967. https://doi.org/10.1109/access.2018.2815149

    Google Scholar 

  21. Coates A, Huval B, Wang T, Wu D, Catanzaro B Y, Ng A (2013) Deep learning with COTS HPC systems. In: International conference on machine learning

  22. Codella N, Gutman D, Celebi M, Helba B, Marchetti M, Dusza S, Kalloo A, Liopyris K, Mishra N, Kittler H, Halpern A (2018) 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). In: 2018 IEEE 15th international symposium on biomedical imaging (ISBI 2018). https://doi.org/10.1109/isbi.2018.8363547

  23. Dai X, Ng J, Davis L (2017) FASON: First and second order information fusion network for texture recognition. In: 2017 IEEE conference on computer vision and pattern recognition (CVPR). https://doi.org/10.1109/cvpr.2017.646

  24. Ding S, Zhu H, Jia W, Su C (2011) A survey on feature extraction for pattern recognition. Artif Intell Rev 37(3):169–180. https://doi.org/10.1007/s10462-011-9225-y

    Google Scholar 

  25. Dorj U, Lee K, Choi J, Lee M (2018) The skin cancer classification using deep convolutional neural network. Multimed Tools Appl 77(8):9909–9924. https://doi.org/10.1007/s11042-018-5714-1

    Google Scholar 

  26. Du C, Gao S (2017) Image segmentation-based multi-focus image fusion through multi-scale convolutional neural network. IEEE Access 5:15750–15761. https://doi.org/10.1109/access.2017.2735019

    Google Scholar 

  27. Duchi J, Hazan E, Singer Y (2011) Adaptive subgradient methods for online learning and stochastic optimization. J Mach Learn Res 12:2121–2159

    MathSciNet  MATH  Google Scholar 

  28. Esteva A, Kuprel B, Novoa R, Ko J, Swetter S, Blau H, Thrun S (2017) Dermatologist-level classification of skin cancer with deep neural networks. Nature 542(7639):115–118. https://doi.org/10.1038/nature21056

    Google Scholar 

  29. Ge Y, Jiang S, Xu Q, Jiang C, Ye F (2017) Exploiting representations from pre-trained convolutional neural networks for high-resolution remote sensing image retrieval. Multimed Tools Appl 77(13):17489–17515. https://doi.org/10.1007/s11042-017-5314-5

    Google Scholar 

  30. Gibson E, Li W, Sudre C, Fidon L, Shakir D, Wang G, Eaton-Rosen Z, Gray R, Doel T, Hu Y, Whyntie T, Nachev P, Modat M, Barratt D, Ourselin S, Cardoso M, Vercauteren T (2018) NiftyNet: a deep-learning platform for medical imaging. Comput Methods Programs Biomed 158:113–122. https://doi.org/10.1016/j.cmpb.2018.01.025

    Google Scholar 

  31. Gogate M, Adeel A, Hussain A (2017) Deep learning driven multimodal fusion for automated deception detection. In: 2017 IEEE symposium series on computational intelligence (SSCI). https://doi.org/10.1109/ssci.2017.8285382

  32. Golrizkhatami Z, Acan A (2018) ECG classification using three-level fusion of different feature descriptors. Expert Syst Appl 114:54–64. https://doi.org/10.1016/j.eswa.2018.07.030

    Google Scholar 

  33. Gong Y, Wang L, Guo R, Lazebnik S (2014) Multi-scale orderless pooling of deep convolutional activation features. In: Computer vision-ECCV 2014 392-407. https://doi.org/10.1007/978-3-319-10584-0_26

  34. Guo Y, Liu Y, Oerlemans A, Lao S, Wu S, Lew M (2016) Deep learning for visual understanding: A review. Neurocomputing 187:27–48. https://doi.org/10.1016/j.neucom.2015.09.116

    Google Scholar 

  35. Gutman D, Codella N, Celebi E, Helba B, Marchetti M, Mishra N, Halpern A (2016) Skin lesion analysis toward melanoma detection: A challenge at the international symposium on biomedical imaging (ISBI) 2016, hosted by the International Skin Imaging Collaboration (ISIC). arXiv:1605.01397

  36. Han L, Hu J, Li L (2013) Structure descriptor for articulated shape analysis. In: International symposium on visual computing 171-180. https://doi.org/10.1007/978-3-642-41914-0_18

  37. Han S, Kim M, Lim W, Park G, Park I, Chang S (2018) Classification of the clinical images for benign and malignant cutaneous tumors using a deep learning algorithm. J Investig Dermatol 138(7):1529–1538. https://doi.org/10.1016/j.jid.2018.01.028

    Google Scholar 

  38. Havaei M, Davy A, Warde-Farley D, Biard A, Courville A, Bengio Y, Pal C, Jodoin P, Larochelle H (2017) Brain tumor segmentation with deep neural networks. Med Image Anal 35:18–31. https://doi.org/10.1016/j.media.2016.05.004

    Google Scholar 

  39. He K, Zhang X, Ren S, Sun J Deep Residual Learning for Image Recognition. In: 2016 IEEE conference on computer vision and pattern recognition (CVPR, vol 2016. https://doi.org/10.1109/cvpr.2016.90

  40. Hou Q, Cheng M, Hu X, Borji A, Tu Z, Torr P Deeply supervised salient object detection with short connections. In: 2017 IEEE conference on computer vision and pattern recognition (CVPR), vol 2017. https://doi.org/10.1109/cvpr.2017.563

  41. Huang G, Liu Z, Maaten L, Weinberger K, vol 2017, Densely connected convolutional networks. In: 2017 IEEE conference on computer vision and pattern recognition (CVPR). https://doi.org/10.1109/cvpr.2017.243

  42. Huo L, Rao T, Zhang L (2018) Fused feature encoding in convolutional neural network. Multimed Tools Appl 78(2):1635–1648. https://doi.org/10.1007/s11042-018-6249-1

    Google Scholar 

  43. Ide H, Kurita T (2016) Low level visual feature extraction by learning of multiple tasks for convolutional neural networks. In: 2016 International joint conference on neural networks (IJCNN). https://doi.org/10.1109/ijcnn.2016.7727665

  44. Jia Y, Shelhamer E, Donahue J, Karayev S, Long J, Girshick R, Guadarrama S, Darrell T (2014) Caffe: convolutional architecture for fast feature embedding. In: ACM international conference on multimedia (MM). https://doi.org/10.1145/2647868.2654889

  45. Kingma D, Ba J (2015) Adam: A Method for Stochastic Optimization. 2015 International Conference on Learning Representations (ICLR). arXiv:1412.6980

  46. Kleinberg R, Li Y, Yuan Y (2018) An alternative view: When Does SGD escape local minima?. In: 2018 International conference on machine learning (ICML). arXiv:1802.06175

  47. Krizhevsky A, Sutskever I, Hinton G (2017) ImageNet classification with deep convolutional neural networks. In: Proceedings of the 25th international conference on neural information processing systems. https://doi.org/10.1145/3065386, vol 1, pp 1097–1105

  48. Kuai Y, Wen G, Li D (2018) Hyper-Siamese network for robust visual tracking. SIViP 13(1):35–42. https://doi.org/10.1007/s11760-018-1325-6

    Google Scholar 

  49. Lacy K, Alwan W (2013) Skin cancer. Medicine 41(7):402–405. https://doi.org/10.1016/j.mpmed.2013.04.008

    Google Scholar 

  50. Lecun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. In: Proceedings of the IEEE. https://doi.org/10.1109/5.726791, vol 86, pp 2278–2324

  51. Lee H, Mendes A, Spolaôr N, Oliva J, Sabino Parmezan A, Wu F, Fonseca-Pinto R (2018) Dermoscopic assisted diagnosis in melanoma: Reviewing results, optimizing methodologies and quantifying empirical guidelines. Knowl-Based Syst 158:9–24. https://doi.org/10.1016/j.knosys.2018.05.016

    Google Scholar 

  52. Lee T, Ng V, Gallagher R, Coldman A, McLean D (1997) Dullrazor: A software approach to hair removal from images. Comput Biol Med 27(6):533–543. https://doi.org/10.1016/s0010-4825(97)00020-6

    Google Scholar 

  53. Li E, Xia J, Du P, Lin C, Samat A (2017) Integrating multilayer features of convolutional neural networks for remote sensing scene classification. IEEE Trans Geosci Remote Sens 55(10):5653–5665. https://doi.org/10.1109/tgrs.2017.2711275

    Google Scholar 

  54. Li F, Zurada J, Liu Y, Wu W (2017) Input layer regularization of multilayer feedforward neural networks. IEEE Access 5:10979–10985. https://doi.org/10.1109/access.2017.2713389

    Google Scholar 

  55. Li H, Weng J, Shi Y, Gu W, Mao Y, Wang Y, Liu W, Zhang J (2018) An improved deep learning approach for detection of thyroid papillary cancer in ultrasound images. Sci Rep 8(1):1–12. https://doi.org/10.1038/s41598-018-25005-7

    Google Scholar 

  56. Li Y, Charalampaki P, Liu Y, Yang G, Giannarou S (2018) Context aware decision support in neurosurgical oncology based on an efficient classification of endomicroscopic data. Int J Comput Assist Radiol Surg 13(8):1187–1199. https://doi.org/10.1007/s11548-018-1806-7

    Google Scholar 

  57. Li Y, Shen L (2018) Skin lesion analysis towards melanoma detection using deep learning network. Sensors 18(2):1–16. https://doi.org/10.3390/s18020556

    Google Scholar 

  58. Liu B, Wei Y, Zhang Y, Yang Q (2017) Deep neural networks for high dimension low sample size data. In: Proceedings of the 26th international joint conference on artificial intelligence. https://doi.org/10.24963/ijcai.2017/318

  59. Liu M, Cheng D, Wang K, Wang Y (2018) Multi-modality cascaded convolutional neural networks for Alzheimer’s disease diagnosis. Neuroinformatics 16 (3/4):295–308. https://doi.org/10.1007/s12021-018-9370-4

    Google Scholar 

  60. Liu W, Wang Z, Liu X, Zeng N, Liu Y, Alsaadi F (2017) A survey of deep neural network architectures and their applications. Neurocomputing 234:11–26. https://doi.org/10.1016/j.neucom.2016.12.038

    Google Scholar 

  61. Lynn N, Kyu Z (2017) Segmentation and Classification of Skin Cancer Melanoma from Skin Lesion Images. In: 2017 18th international conference on parallel and distributed computing, applications and technologies (PDCAT). https://doi.org/10.1109/pdcat.2017.00028

  62. Ma C, Huang J, Yang X, Yang M (2015) Hierarchical convolutional features for visual tracking. In: 2015 IEEE International Conference on Computer Vision (ICCV). https://doi.org/10.1109/iccv.2015.352

  63. Ma C, Huang J, Yang X, Yang M (2018) Robust visual tracking via hierarchical convolutional features. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1–1. https://doi.org/10.1109/tpami.2018.2865311

  64. Maas A, Hannun A, Ng A (2013) Rectifier nonlinearities improve neural network acoustic models. In: 2013 international conference on machine learning (ICML)

  65. Majumder N, Hazarika D, Gelbukh A, Cambria E, Poria S (2018) Multimodal sentiment analysis using hierarchical fusion with context modeling. Knowl-Based Syst 161:124–133. https://doi.org/10.1016/j.knosys.2018.07.041

    Google Scholar 

  66. Mash R, Borghetti B, Pecarina J (2016) Improved aircraft recognition for aerial refueling through data augmentation in convolutional neural networks. In: Advances in visual computing 113-122. https://doi.org/10.1007/978-3-319-50835-1_11

  67. Matsugu M, Cardon P (2004) Unsupervised feature selection for multi-class object detection using convolutional neural networks. In: International symposium on neural networks 864-869. https://doi.org/10.1007/978-3-540-28647-9_142

  68. Menegola A, Fornaciali M, Pires R, Bittencourt F, Avila S, Valle E (2017) Knowledge transfer for melanoma screening with deep learning. In: 2017 IEEE 14th international symposium on biomedical imaging (ISBI 2017). https://doi.org/10.1109/isbi.2017.7950523

  69. Mukkamala M, Hein M (2017) Variants of RMSProp and Adagrad with logarithmic regret bounds. In: 2017 international conference on machine learning (ICML). arXiv:1706.05507

  70. Nickerson P, Tighe P, Shickel B, Rashidi P (2016) Deep neural network architectures for forecasting analgesic response. In: 2016 38th annual international conference of the ieee engineering in medicine and biology society (EMBC). https://doi.org/10.1109/embc.2016.7591352

  71. Popescu D, Ichim L (2015) Image recognition in UAV application based on texture analysis. In: International conference on advanced concepts for intelligent vision systems 693-704. https://doi.org/10.1007/978-3-319-25903-1_60

  72. Shie C, Chuang C, Chou C, Wu M, Chang E (2015) Transfer representation learning for medical image analysis. In: 2015 37th annual international conference of the ieee engineering in medicine and biology society (EMBC). https://doi.org/10.1109/embc.2015.7318461

  73. Shin H, Roth H, Gao M, Lu L, Xu Z, Nogues I, Yao J, Mollura D, Summers R (2016) Deep convolutional neural networks for computer-aided detection: CNN Architectures, dataset characteristics and transfer learning. IEEE Trans Med Imaging 35(5):1285–1298. https://doi.org/10.1109/tmi.2016.2528162

    Google Scholar 

  74. Simonyan K, Zisserman A (2015) Very deep convolutional networks for large-scale image recognition. In: 2015 International conference on learning representations (ICLR). arXiv:1409.1556

  75. Sun A, Lim E (2001) Hierarchical text classification and evaluation. In: Proceedings 2001 IEEE international conference on data mining. https://doi.org/10.1109/icdm.2001.989560

  76. Sun J, Wan C, Cheng J, Yu F, Liu J (2017) Retinal image quality classification using fine-tuned CNN. In: International workshop on ophthalmic medical image analysis 126-133. https://doi.org/10.1007/978-3-319-67561-9_14

  77. Tang J, Mat Isa N (2014) Adaptive image enhancement based on bi-histogram equalization with a clipping limit. Comput Electr Eng 40(8):86–103. https://doi.org/10.1016/j.compeleceng.2014.05.017

    Google Scholar 

  78. Taqi A, Awad A, Al-Azzo F, Milanova M (2018) The impact of multi-optimizers and data augmentation on tensorflow convolutional neural network performance. In: 2018 IEEE conference on multimedia information processing and retrieval (MIPR). https://doi.org/10.1109/mipr.2018.00032

  79. Thao L, Quang N (2017) Automatic skin lesion analysis towards melanoma detection. In: 2017 21st Asia pacific symposium on intelligent and evolutionary systems (IES). https://doi.org/10.1109/iesys.2017.8233570

  80. Toader M, Esanu I, Taranu T, Toader S (2017) Utility of polarized dermoscopy in the diagnosis of cutaneous lupus erythematosus and morphea. In: 2017 E-health and bioengineering conference (EHB). https://doi.org/10.1109/ehb.2017.7995491

  81. Tripathi G, Singh K, Vishwakarma D (2018) Convolutional neural networks for crowd behaviour analysis: a survey. Vis Comput 35 (5):753–776. https://doi.org/10.1007/s00371-018-1499-5

    Google Scholar 

  82. Tschandl P, Rosendahl C, Kittler H (2018) The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions. Scientific Data 5:1–9. https://doi.org/10.1038/sdata.2018.161

    Google Scholar 

  83. Tschandl P, Sinz C, Kittler H (2019) Domain-specific classification-pretrained fully convolutional network encoders for skin lesion segmentation. Comput Biol Med 104:111–116. https://doi.org/10.1016/j.compbiomed.2018.11.010

    Google Scholar 

  84. Vu T, Ho N, Yang H, Kim J, Song H (2018) Non-white matter tissue extraction and deep convolutional neural network for Alzheimer’s disease detection. Soft Comput 22(20):6825–6833. https://doi.org/10.1007/s00500-018-3421-5

    Google Scholar 

  85. Wan M, Lai Z, Yang G, Yang Z, Zhang F, Zheng H (2017) Local graph embedding based on maximum margin criterion via fuzzy set. Fuzzy Set Syst 318:120–131. https://doi.org/10.1016/j.fss.2016.06.001

    MathSciNet  Google Scholar 

  86. Wan M, Li M, Yang G, Gai S, Jin Z (2014) Feature extraction using two-dimensional maximum embedding difference. Inform Sci 274:55–69. https://doi.org/10.1016/j.ins.2014.02.145

    Google Scholar 

  87. Wan M, Yang G, Gai S, Yang Z (2015) Two-dimensional discriminant locality preserving projections (2DDLPP) and its application to feature extraction via fuzzy set. Multimed Tools Appl 76(1):355–371. https://doi.org/10.1007/s11042-015-3057-8

    Google Scholar 

  88. Wang X, Hua X, Xiao F, Li Y, Hu X, Sun P (2018) Multi-object detection in traffic scenes based on improved SSD. Electronics 7(11):1–28. https://doi.org/10.3390/electronics7110302

    Google Scholar 

  89. Wei Y, Zhao Y, Lu C, Wei S, Liu L, Zhu Z, Yan S (2016) Cross-modal retrieval with CNN visual features: A new baseline. IEEE Transactions on Cybernetics 47(2):449–460. https://doi.org/10.1109/tcyb.2016.2519449

    Google Scholar 

  90. Wen J, Ye F, Huang X, Li S, Yang L, Xiao X, Xie X (2015) Prognostic significance of preoperative circulating monocyte count in patients with breast cancer. Medicine 94(49):1–7. https://doi.org/10.1097/md.0000000000002266

    Google Scholar 

  91. Wu Z, Singh B, Davis L, Subrahmanian V (2017) Deception detection in videos. arXiv:1712.04415

  92. Xu J, Zhang Z, Friedman T, Liang Y, Broeck G (2018) A semantic loss function for deep learning with symbolic knowledge. In: Proceedings of machine learning research (PMLR). arXiv:1711.11157

  93. Yang X, Bian C, Yu L, Ni D, Heng P (2018) Class-balanced deep neural network for automatic ventricular structure segmentation. In: International workshop on statistical atlases and computational models of the heart 152-160. https://doi.org/10.1007/978-3-319-75541-0_16

  94. Yang Y, Shah M (2014) Learning discriminative features and metrics for measuring action similarity. In: 2014 IEEE international conference on image processing (ICIP). https://doi.org/10.1109/icip.2014.7025311

  95. Ye F, Pu J, Wang J, Li Y, Zha H (2017) Glioma grading based on 3D multimodal convolutional neural network and privileged learning. In: 2017 IEEE international conference on bioinformatics and biomedicine (BIBM). https://doi.org/10.1109/bibm.2017.8217751

  96. Yosinski J, Clune J, Bengio Y, Lipson H (1792) How transferable are features in deep neural networks?. In: International Conference on Neural Information Processing Systems. arXiv:1411.1792

  97. Yu C, Yang S, Kim W, Jung J, Chung K, Lee S, Oh B (2018) Acral melanoma detection using a convolutional neural network for dermoscopy images. PLOS ONE 13(3):1–14. https://doi.org/10.1371/journal.pone.0196621

    Google Scholar 

  98. Yu D, Liu Y, Pang Y, Li Z, Li H (2018) A multi-layer deep fusion convolutional neural network for sketch based image retrieval. Neurocomputing 296:23–32. https://doi.org/10.1016/j.neucom.2018.03.031

    Google Scholar 

  99. Yu W, Yang K, Yao H, Sun X, Xu P (2017) Exploiting the complementary strengths of multi-layer CNN features for image retrieval. Neurocomputing 237:235–241. https://doi.org/10.1016/j.neucom.2016.12.002

    Google Scholar 

  100. Yu Z, Jiang X, Zhou F, Qin J, Ni D, Chen S, Lei B, Wang T (2018) Melanoma recognition in dermoscopy images via aggregated deep convolutional features. IEEE Trans Biomed Eng 66(4):1006–1016. https://doi.org/10.1109/tbme.2018.2866166

    Google Scholar 

  101. Zhang K, Guo L, Gao C (2018) Optimization method of residual networks of residual networks for image classification. In: 2018 IEEE international conference on big data and smart computing (BigComp). https://doi.org/10.1109/bigcomp.2018.00054

  102. Zhang X, Wang S, Liu J, Tao C (2018) Towards improving diagnosis of skin diseases by combining deep neural network and human knowledge. BMC Med Inform Decis Mak 18(2):1–8. https://doi.org/10.1186/s12911-018-0631-9

    Google Scholar 

  103. Zhang Y, Muhammad K, Tang C (2018) Twelve-layer deep convolutional neural network with stochastic pooling for tea category classification on GPU platform. Multimed Tools Appl 77(17):22821–22839. https://doi.org/10.1007/s11042-018-5765-3

    Google Scholar 

  104. Zhao R, Ouyang W, Li H, Wang X (2015) Saliency detection by multi-context deep learning. In: 2015 IEEE conference on computer vision and pattern recognition (CVPR). https://doi.org/10.1109/cvpr.2015.7298731

  105. Zheng L, Zhao Y, Wang S, Wang J, Tian Q (2016) Good practice in CNN feature transfer. arXiv:1604.00133

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  1. LabSIV, Department of Computer Science, Faculty of Science, Ibn Zohr University, Agadir, Morocco

    Ibtissam Bakkouri & Karim Afdel

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  1. Ibtissam Bakkouri
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  2. Karim Afdel
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Correspondence to Ibtissam Bakkouri.

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Bakkouri, I., Afdel, K. Computer-aided diagnosis (CAD) system based on multi-layer feature fusion network for skin lesion recognition in dermoscopy images. Multimed Tools Appl 79, 20483–20518 (2020). https://doi.org/10.1007/s11042-019-07988-1

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