Skip to main content

Advertisement

SpringerLink
Log in

Automatic skin lesion classification using a new densely connected convolutional network with an SF module

  • Original Article
  • Published:
Medical & Biological Engineering & Computing Aims and scope Submit manuscript

Abstract

The automatic classification of skin lesions in dermoscopy images remains challenging due to the morphological diversity of skin lesions, the existence of intrinsic cutaneous features and artefacts, the lack of training data, and the insufficient recognition abilities of current methods. To meet these challenges, we construct a new densely connected convolutional network termed DenseSFNet-45, which is obtained by integrating our proposed novel architectural unit (an SE-Fire (SF) block) into the dense block of a dense convolutional network (DenseNet). The SF block consists of a cascade of a Fire module and a squeeze-and-excitation (SE) block, enhancing the representational power of DenseNet by exploiting both spatial and channel-wise information. Based on DenseSFNet, we propose a novel two-stage framework consisting of skin lesion segmentation followed by lesion classification to accurately classify skin lesions. The classification step is performed on the segmented lesion rather than the whole dermoscopy image, enabling the classification network to extract more specific and discriminative features. The proposed method is extensively evaluated on three public databases: ISBI 2017 Skin Lesion Analysis Towards Melanoma Detection Challenge dataset (ISBI-skin-2017), ISBI 2018 Skin Lesion Analysis Towards Melanoma Detection Challenge dataset (ISBI-skin-2018), and PH2 dataset. The experimental results demonstrate the superior performance of our method relative to that of the traditional machine learning algorithms, the existing classical classification models, baselines, and state-of-the-art methods.

Graphical abstract

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  1. Jerant AF, Johnson JT, Sheridan CD, Caffrey TJ (2000) Early detection and treatment of skin cancer. American Family Physician 62(2):357–368

    CAS  PubMed  Google Scholar 

  2. Siegel RL, Miller KD, Jemal A (2019) Cancer statistics, 2019. CA: A Cancer Journal for Clinicians 69(1):7–34

    Google Scholar 

  3. Freedberg KA, Geller AC, Miller DR, Lew RA, Koh HK (1999) Screening for malignant melanoma: a cost-effectiveness analysis. Journal of the American Academy of Dermatology 41(5):738–745

    Article  CAS  Google Scholar 

  4. Balch CM, Buzaid AC, Soong SJ, Atkins MB, Cascinelli N, Coit DG, Fleming ID, Gershenwald JE, Houghton A Jr, Kirkwood JM et al (2001) Final version of the American joint committee on cancer staging system for cutaneous melanoma. Journal of Clinical Oncology 19(16):3635–3648

    Article  CAS  Google Scholar 

  5. Braun RP, Rabinovitz HS, Oliviero M, Kopf AW, Saurat JH (2005) Dermoscopy of pigmented skin lesions. Journal of the American Academy of Dermatology 52(1):109–121

    Article  Google Scholar 

  6. Celebi ME, Kingravi HA, Uddin B, Iyatomi H, Aslandogan YA, Stoecker WV, Moss RH (2007) A methodological approach to the classification of dermoscopy images. Computerized Medical Imaging and Graphics 31(6):362–373

    Article  Google Scholar 

  7. Schaefer G, Krawczyk B, Celebi ME, Iyatomi H (2014) An ensemble classification approach for melanoma diagnosis. Memetic Computing 6(4):233–240

    Article  Google Scholar 

  8. Murugan A, Nair SAH, Kumar KS (2019) Detection of skin cancer using svm, random forest and knn classifiers. Journal of Medical Systems 43(8):1–9

    Article  Google Scholar 

  9. Zaqout I (2019) Diagnosis of skin lesions based on dermoscopic images using image processing techniques. Pattern Recognition-Selected Methods and Applications

  10. Almaraz-Damian JA, Ponomaryov V, Sadovnychiy S, Castillejos-Fernandez H (2020) Melanoma and nevus skin lesion classification using handcraft and deep learning feature fusion via mutual information measures. Entropy 22(4):484

    Article  Google Scholar 

  11. Dhivyaa C, Sangeetha K, Balamurugan M, Amaran S, Vetriselvi T, Johnpaul P (2020) Skin lesion classification using decision trees and random forest algorithms. Journal of Ambient Intelligence and Humanized Computing pp 1–13

  12. LeCun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11):2278–2324

    Article  Google Scholar 

  13. He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770–778

  14. Barata C, Celebi ME, Marques JS (2021) Explainable skin lesion diagnosis using taxonomies. Pattern Recognition 110:107413

    Article  Google Scholar 

  15. Xie Y, Xia Y, Zhang J, Song Y, Feng D, Fulham M, Cai W (2018) Knowledge-based collaborative deep learning for benign-malignant lung nodule classification on chest ct. IEEE Transactions on Medical Imaging 38(4):991–1004

    Article  Google Scholar 

  16. Zhang J, Xie Y, Wu Q, Xia Y (2018) Skin lesion classification in dermoscopy images using synergic deep learning. In: International conference on medical image computing and computer-assisted intervention. Springer, pp 12–20

  17. Long J, Shelhamer E, Darrell T (2015) Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3431–3440

  18. Chen LC, Papandreou G, Kokkinos I, Murphy K, Yuille AL (2017) Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. IEEE transactions on pattern analysis and machine intelligence 40(4):834–848

    Article  Google Scholar 

  19. He K, Gkioxari G, Dollár P, Girshick R (2017) Mask r-cnn. In: Proceedings of the IEEE international conference on computer vision. pp 2961–2969

  20. Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision. pp 2980–2988

  21. 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 Transactions on Biomedical Engineering 66(4):1006–1016

    Article  Google Scholar 

  22. Mahbod A, Schaefer G, Ellinger I, Ecker R, Pitiot A, Wang C (2019) Fusing fine-tuned deep features for skin lesion classification. Computerized Medical Imaging and Graphics 71:19–29

    Article  Google Scholar 

  23. Zhang J, Xie Y, Xia Y, Shen C (2019) Attention residual learning for skin lesion classification. IEEE Transactions on Medical Imaging 38(9):2092–2103

    Article  Google Scholar 

  24. Hosny KM, Kassem MA, Fouad MM (2020) Classification of skin lesions into seven classes using transfer learning with alexnet. Journal of Digital Imaging 33(5):1325–1334

    Article  Google Scholar 

  25. Narin A (2021) Accurate detection of covid-19 using deep features based on x-ray images and feature selection methods. Computers in Biology and Medicine 137:104771

    Article  CAS  Google Scholar 

  26. Razzak I, Naz S (2020) Unit-vise: Deep shallow unit-vise residual neural networks with transition layer for expert level skin cancer classification. IEEE/ACM Transactions on Computational Biology and Bioinformatics

  27. Yu L, Chen H, Dou Q, Qin J, Heng PA (2016) Automated melanoma recognition in dermoscopy images via very deep residual networks. IEEE Transactions on Medical Imaging 36(4):994–1004

    Article  Google Scholar 

  28. Al-Masni MA, Kim DH, Kim TS (2020) Multiple skin lesions diagnostics via integrated deep convolutional networks for segmentation and classification. Computer Methods and Programs in Biomedicine 190:105351

    Article  Google Scholar 

  29. Huang G, Liu Z, Van Der Maaten L, Weinberger KQ (2017) Densely connected convolutional networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp 4700–4708

  30. Zhang K, Guo Y, Wang X, Yuan J, Ding Q (2019) Multiple feature reweight densenet for image classification. IEEE Access 7:9872–9880

    Article  Google Scholar 

  31. Tao Y, Xu M, Lu Z, Zhong Y (2018) Densenet-based depth-width double reinforced deep learning neural network for high-resolution remote sensing image per-pixel classification. Remote Sensing 10(5):779

    Article  Google Scholar 

  32. Liang S, Zhang R, Liang D, Song T, Ai T, Xia C, Xia L, Wang Y (2018) Multimodal 3d densenet for idh genotype prediction in gliomas. Genes 9(8):382

    Article  Google Scholar 

  33. Iandola FN, Han S, Moskewicz MW, Ashraf K, Dally WJ, Keutzer K (2016) Squeezenet: Alexnet-level accuracy with 50x fewer parameters and \(<\) 0.5 mb model size. arXiv:1602.07360

  34. Hu J, Shen L, Sun G (2018) Squeeze-and-excitation networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp 7132–7141

  35. Shan P, Wang Y, Fu C, Song W, Chen J (2020) Automatic skin lesion segmentation based on fc-dpn. Comput Biol Med 123:103762

  36. Codella NC, Gutman D, Celebi ME, Helba B, Marchetti MA, Dusza SW, Kalloo A, Liopyris K, Mishra N, Kittler H, et al (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). IEEE, pp 168–172

  37. 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):1–9

    Article  Google Scholar 

  38. Codella N, Rotemberg V, Tschandl P, Celebi ME, Dusza S, Gutman D, Helba B, Kalloo A, Liopyris K, Marchetti M, et al (2019) Skin lesion analysis toward melanoma detection 2018: A challenge hosted by the international skin imaging collaboration (isic). arXiv:1902.03368

  39. Mendonça T, Ferreira PM, Marques JS, Marcal AR, Rozeira J (2013) Ph 2-a dermoscopic image database for research and benchmarking. In: 2013 35th annual international conference of the IEEE engineering in medicine and biology society (EMBC). IEEE, pp 5437–5440

  40. Lin M, Chen Q, Yan S (2013) Network in network. arXiv:1312.4400

  41. Glorot X, Bordes A, Bengio Y (2011) Deep sparse rectifier neural networks. In: Proceedings of the fourteenth international conference on artificial intelligence and statistics. pp 315–323

  42. Bottou L (2012) Stochastic gradient descent tricks. In: Neural networks: Tricks of the trade. Springer, pp 421–436

  43. Gutman D, Codella NC, 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

  44. Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems. pp 1097–1105

  45. Woo S, Park J, Lee JY, Kweon IS (2018) Cbam: Convolutional block attention module. In: Proceedings of the European conference on computer vision (ECCV). pp 3–19

  46. Selvaraju RR, Cogswell M, Das A, Vedantam R, Parikh D, Batra D (2017) Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE international conference on computer vision. pp 618–626

  47. Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556

  48. Chollet F (2017) Xception: Deep learning with depthwise separable convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp 1251–1258

  49. Xie S, Girshick R, Dollár P, Tu Z, He K (2017) Aggregated residual transformations for deep neural networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp 1492–1500

  50. Tan M, Le Q (2019) Efficientnet: Rethinking model scaling for convolutional neural networks. In: International Conference on Machine Learning, PMLR, pp 6105–6114

  51. Mahbod A, Schaefer G, Wang C, Ecker R, Ellinge I (2019) Skin lesion classification using hybrid deep neural networks. In: ICASSP 2019-2019 IEEE international conference on acoustics, speech and signal processing (ICASSP). IEEE, pp 1229–1233

  52. Harangi B (2018) Skin lesion classification with ensembles of deep convolutional neural networks. J Biomed Inform 86:25–32

Download references

Funding

This work was supported by the National Natural Science Foundation of China (Nos. 61773068 and 61671141) and the Fundamental Research Funds for the Central Universities (No. N2224001-7).

Author information

Authors and Affiliations

  1. School of Computer Science and Engineering, Northeastern University, Shenyang, 110819, China

    Pufang Shan, Chong Fu, Liming Dai, Tihui Jia & Jun Liu

  2. Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, 110819, China

    Chong Fu

  3. Engineering Research Center of Security Technology of Complex Network System, Ministry of Education, Shenyang, China

    Chong Fu

  4. Science and Technology on Space Physics Laboratory, Beijing, 100076, China

    Ming Tie

Authors
  1. Pufang Shan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Chong Fu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Liming Dai
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Tihui Jia
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Ming Tie
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Jun Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Chong Fu.

Ethics declarations

Competing interests

The authors declare no competing interests.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file 1 (pdf 67766 KB)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Shan, P., Fu, C., Dai, L. et al. Automatic skin lesion classification using a new densely connected convolutional network with an SF module. Med Biol Eng Comput 60, 2173–2188 (2022). https://doi.org/10.1007/s11517-022-02583-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11517-022-02583-3

Keywords

Search

Navigation