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An automated multi-class skin lesion diagnosis by embedding local and global features of Dermoscopy images

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Abstract

Skin cancer is an increasing cause of concern among cancers worldwide. There has been extensive research carried out all over the globe for the early detection of skin cancer to increase the life expectancy of patients. The decision support systems and Computer-aided diagnosis systems aid in detecting cancer at an early stage. The increasing ability of Convolutional Neural Networks (CNN) to extract delicate patterns has made it a popular choice in automated decision support systems. This work proposes a novel U-Net segmentation network with Spatial Attention Blocks (SPAB) called SASegNet to segment the skin lesion accurately. The spatial attention blocks emphasize the model to focus on a particular region. The proposed SASegNet model can provide an accuracy of 95% on the PH2 dataset. In this work, EfficientNet B1 is used for classification. The local features from segmentation results are then passed to EfficientNet B1 to extract features for classification. The pre-processed original images are passed to EfficientNet B1 to extract the global features. Finally, these two features are concatenated to extract the best patterns for classification. Experimentation is carried out on the International Skin Imaging Collaboration (ISIC) datasets. The proposed methodology can obtain the Area Under Curve Receiver Operating Characteristic Curve (AUC-ROC) as 0.974, 0.972, 0.962, and 0.937 for the ISIC-2017, 18, 19, and 2020 datasets. The results obtained are the benchmark results to the best of our knowledge. This automated methodology can aid practising dermatologists in a robust diagnosis.

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Data availability

All datasets used in the study are publicly available from the ISIC website https://challenge.isic-archive.com/data/#2018.

Abbreviations

SPAB:

SPatial Attention Block

CNN:

Convolutional Neural Networks

IoU:

Intersection over Union

AUC-ROC:

Area Under Curve Receiver Operating Characteristic Curve

CoE:

Center of Excellence

SPARC:

Scheme for Promotion of Academic and Research Collaboration

MoE:

Ministry of Education

CAD:

Computer-Aided Diagnosis

DL:

Deep Learning

DCNN:

Deep Convolutional Neural Networks

MCSCC:

Multi-Class Skin Cancer Classification

KNN:

K- Nearest Neighbors

SVM:

support vector machine

ANN:

artificial neural network

ISBI:

International Symposium on Biomedical Imaging

ROC:

receiver operating characteristic curve

ISIC:

International Skin Imaging Collaboration

HAM10000:

Human Against the Machine

RCNN:

Region-based Convolutional Neural Network

AUC:

area under the curve

AKIEC:

Actinic Keratosis

BCC:

Basal cell carcinoma

BKL:

Benign keratosis

DF:

Dermatofibroma

NV:

Melanocytic nevi

MEL:

Melanoma

VASC:

Vascular lesions

SCC:

Squamous cell carcinoma

SASegNet:

Spatial Attention Segmentation Network

CM:

Confusion Matrix

TP:

True Positive

TN:

True Negative

FP:

False Positive

FN:

False Negative

DC:

Dice Coefficient

SGD:

Stochastic Gradient Descent

TPR:

True Positive Rate

FPR:

False Positive Rate

References

  1. Almseidin M, Abu Zuraiq A, Al-kasassbeh M, Alnidami N (2019) Phishing detection based on machine learning and feature selection methods. Int J Interact Mobile Technol (iJIM) 13(12):171–183. https://doi.org/10.3991/ijim.v13i12.11411

    Article  Google Scholar 

  2. Ballerini L, Fisher RB, Aldridge B, Rees J (2013) A color and texture based hierarchical K-NN approach to the classification of non-melanoma skin lesions. In: Color medical image analysis. Springer, Dordrecht, pp 63–86

    Chapter  Google Scholar 

  3. Bi L, Kim J, Ahn E, Kumar A, Feng D, Fulham M (2019) Step-wise integration of deep class-specific learning for dermoscopic image segmentation. Pattern Recogn 85:78–89

    Article  Google Scholar 

  4. Cancer Facts and Figures 2021. American Cancer Society. https://www.cancer.org/content/dam/cancer-org/research/cancer-facts-and statistics/annual-cancer-facts-and-figures/2021/cancer-facts-and-figures-2021.pdf. Accessed January 13, 2021.

  5. Celebi ME, Kingravi HA, Uddin B, Iyatomi H, Aslandogan YA, Stoecker WV, Moss RH (2007) A methodological approach to the classification of dermoscopy images. Comput Med Imaging Graph : Off J Comput Med Imaging Soc 31(6):362–373. https://doi.org/10.1016/j.compmedimag.2007.01.003

    Article  Google Scholar 

  6. Celebi ME, Iyatomi H, Stoecker WV, Moss RH, Rabinovitz HS, Argenziano G, Soyer HP (2008) Automatic detection of blue-white veil and related structures in dermoscopy images. Comput Med Imaging Graphics : Off J Comput Med Imaging Soc 32(8):670–677. https://doi.org/10.1016/j.compmedimag.2008.08.003S

    Article  Google Scholar 

  7. Celebi ME, Iyatomi H, Stoecker WV, Moss RH, Rabinovitz HS, Argenziano G, Soyer HP (2008) Automatic detection of blue-white veil and related structures in dermoscopy images. Comput Med Imaging Graph : Off J Comput Med Imaging Soc 32(8):670–677. https://doi.org/10.1016/j.compmedimag.2008.08.003

    Article  Google Scholar 

  8. Chaturvedi SS, Tembhurne JV, Diwan T (2020) A multi-class skin cancer classification using deep convolutional neural networks. Multimed Tools Appl 79(39–40):28477–28498

    Article  Google Scholar 

  9. Chaturvedi SS, Gupta K, Prasad PS (2021) Skin Lesion Analyser: An Efficient Seven-Way Multi-class Skin Cancer Classification Using MobileNet. In: Hassanien A., Bhatnagar R., Darwish A. (eds) Advanced Machine Learning Technologies and Applications. AMLTA 2020. Advances in intelligent systems and computing, vol 1141. Springer, Singapore https://doi.org/10.1007/978-981-15-3383-9_15.

  10. Chen X, Yao L, Zhang Y (2020). Residual Attention U-Net for Automated Multi-Class Segmentation of COVID-19 Chest CT Images. http://arxiv.org/abs/2004.05645

  11. Chen B, Liu Y, Zhang Z, Lu G, Kong AWK (2021) TransAttUNet: Multi-level Attention-guided U-Net with Transformer for Medical Image Segmentation. http://arxiv.org/abs/2107.05274.

  12. Chen J, Lu Y, Yu Q, Luo X, Adeli E, Wang Y, Lu L, Yuille AL, Zhou Y (2021) TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation. http://arxiv.org/abs/2102.04306

  13. Codella NC, Gutman D, Celebi ME, Helba B, Marchetti MA, Dusza SW, Kalloo A, Liopyris K, Mishra N, Kittler H, Halpern A (2017) 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). arXiv. https://doi.org/10.48550/arXiv.1710.05006

  14. Combalia M, Codella NC, Rotemberg V, Helba B, Vilaplana V, Reiter O, Carrera C, Barreiro A, Halpern AC, Puig S, Malvehy J (2019) BCN20000: Dermoscopic lesions in the wild. arXiv. https://doi.org/10.48550/arXiv.1908.02288

  15. Dabass M, Vashisth S, Vig R (2021) Attention-Guided deep attrous-residual U-Net architecture for automated gland segmentation in colon histopathology images Informatics in Medicine Unlocked:27. https://doi.org/10.1016/j.imu.2021.100784

  16. Gessert N, … Schlaefer A (2020) Skin lesion classification using CNNs with patch-based attention and diagnosis-guided loss weighting. IEEE Trans Biomed Eng 67(2):495–503

    Article  Google Scholar 

  17. Gessert N, Sentker T, Madesta F, Schmitz R, Kniep H, Baltruschat I, Werner R, Schlaefer A (2020) Skin lesion classification using CNNs with patch-based attention and diagnosis-guided loss weighting. IEEE Trans Biomed Eng 67:495–503

    Article  Google Scholar 

  18. Harangi B (2018) Skin lesion classification with ensembles of deep convolutional neural networks. J Biomed Inform 86:25–32. https://doi.org/10.1016/j.jbi.2018.08.006

    Article  Google Scholar 

  19. Harangi B (2018) Skin lesion classification with ensembles of deep convolutional neural networks. J Biomed Inform 86:25–32. https://doi.org/10.1016/j.jbi.2018.08.006

    Article  Google Scholar 

  20. Iqbal I, Younus M, Walayat K, Kakar MU, Ma J (2020) Automated multi-class classification of skin lesions through the deep convolutional neural network with dermoscopic images. Comput. Med. Imaging Graph., vol. 88, no. December, p. 101843

  21. Jain AK, Gupta BB (2016) Comparative analysis of features based machine learning approaches for phishing detection. In: 2016 3rd international conference on computing for sustainable global development (INDIACom), pp 2125–2130

    Google Scholar 

  22. Jha D, Riegler MA, Johansen D, Halvorsen P, Johansen HD (2020) DoubleU-Net: A Deep Convolutional Neural Network for Medical Image Segmentation. http://arxiv.org/abs/2006.04868.

  23. Ji Y, Zhang R, Wang H, Li Z, Wu L, Zhang S, Luo P (2021) Multi-Compound Transformer for Accurate Biomedical Image Segmentation. http://arxiv.org/abs/2106.14385

  24. Jose JM, Sindagi V, Hacihaliloglu I, Patel VM (2020). KiU-Net: Towards Accurate Segmentation of Biomedical Images using Over-complete Representations. http://arxiv.org/abs/2006.04878.

  25. Khan MA, Sharif M, Akram T, Damaševičius R, Maskeliūnas R (2021) Skin Lesion Segmentation and Multiclass Classification Using Deep Learning Features and Improved Moth Flame Optimization. Diagnostics 11, 811(5). https://doi.org/10.3390/diagnostics11050811

  26. Kostopoulos A, Asvestas PA, Kalatzis IK, Sakellaropoulos GC, Sakkis TH, Cavouras DA, Glotsos DT (2017) Adaptable pattern recognition system for discriminating melanocytic nevi from malignant melanomas using plain photography images from different image databases. Int J Med Inform 105:1–10

    Article  Google Scholar 

  27. Krizhevsky A, Sutskever I, Hinton G (2017) Imagenet classification with deep convolutional neural networks. Commun ACM 60(6):89–40

    Article  Google Scholar 

  28. Lakhani P, Gray DL, Pett CR, Nagy P, Shih G (2018) Hello world deep learning in medical imaging. J Digit Imaging 31:283–289

    Article  Google Scholar 

  29. Li S, Qin D, Wu X, Li J, Li B, Han W (2022) False alert detection based on deep learning and machine learning. Int J Semantic Web Inform Syst (IJSWIS) 18(1):1–21

    Article  Google Scholar 

  30. Liu L, Mou L, Zhu XX, Mandal M (2020) Automatic skin lesion classification based on mid-level feature learning. Comput Med Imaging Graph:84. https://doi.org/10.1016/j.compmedimag.2020.101765

  31. Liu X, He J, Song L, Liu S, Srivastava G (2021) Medical image classification based on an adaptive size deep learning model. ACM Trans. Multimedia Comput. Commun. Appl. 17, 3s, article 102:18 pages

  32. Maglogiannis I, Doukas CN (2009) Overview of advanced computer vision Systems for Skin Lesions Characterization. IEEE Trans Inform Technol Biomed 13(5):721–733. https://doi.org/10.1109/TITB.2009.2017529

    Article  Google Scholar 

  33. Mahbod A, Schaefer G, Ellinger I, Ecker R, Pitiot A, Wang C (2019) Fusing fine-tuned deep features for skin lesion classification. Comput Med Imaging Graph 71:19–29. https://doi.org/10.1016/J.COMPMEDIMAG.2018.10.007

    Article  Google Scholar 

  34. Mahbod A, Schaefer G, Ellinger I, Ecker R, Pitiot A, Wang C (2019) Fusing fine-tuned deep features for skin lesion classification. Comput Med Imaging Graphics : Off J Comput Med Imaging Soc 71:19–29. https://doi.org/10.1016/j.compmedimag.2018.10.007

    Article  Google Scholar 

  35. Mishra A, Gupta BB, Peraković D, Peñalvo FJG, Hsu C-H (2021) Classification Based Machine Learning for Detection of DDoS attack in Cloud Computing. 2021 IEEE Int Conf Consum Electron (ICCE), 1–4 https://doi.org/10.1109/ICCE50685.2021.9427665.

  36. Mubashar M, Ali H, Grönlund C, Azmat S (2022) R2U++: a multiscale recurrent residual U-net with dense skip connections for medical image segmentation. Neural Comput Applic 34:17723–17739. https://doi.org/10.1007/s00521-022-07419-7

    Article  Google Scholar 

  37. Nida N, Irtaza A, Javed A, Yousaf MH, Mahmood MT (2019) Melanoma lesion detection and segmentation using deep region based convolutional neural network and fuzzy C-means clustering. Int J Med Inform 124:37–48. https://doi.org/10.1016/j.ijmedinf.2019.01.005

    Article  Google Scholar 

  38. Nyíri T, Kiss A (2018) Novel Ensembling methods for dermatological image classification. In: International conference on theory and practice of natural computing. Springer, Cham, pp. 438–448.

  39. Oktay O, Schlemper J, le Folgoc L, Lee M, Heinrich M, Misawa K, Mori K, McDonagh S, Hammerla NY, Kainz B, Glocker B, Rueckert D (2018). Attention U-Net: Learning Where to Look for the Pancreas. http://arxiv.org/abs/1804.03999.

  40. Punn NS, Agarwal S (2022) RCA-IUNet: a residual cross-spatial attention-guided inception U-net model for tumor segmentation in breast ultrasound imaging. Mach Vis Appl 33(2). https://doi.org/10.1007/s00138-022-01280-3

  41. Qin X, Zhang Z, Huang C, Dehghan M, Zaiane OR, Jagersand M (2020) U2-net: going deeper with nested U-structure for salient object detection. https://doi.org/10.1016/j.patcog.2020.107404

  42. Rahman Z, Hossain MS, Islam MR, Hasan MM, Hridhee RA (2021) An approach for multiclass skin lesion classification based on ensemble learning. Informatics Med Unlocked 25:100659

    Article  Google Scholar 

  43. Ramella G (2021) Saliency-based segmentation of dermoscopic images using colour information, computer methods in biomechanics and biomedical. Engineering, Imaging & Visualization

    Google Scholar 

  44. Ratul AR, Mozaffari MH, Lee WS, Parimbelli E (2019) Skin lesions classification using deep learning based on dilated convolution bioRxiv:860700. https://doi.org/10.1101/860700

  45. Rebouças Filho PP, Peixoto SA (2018) Automatic histologically-closer classification of skin lesions. Comput. Med. Imaging Graph 68:40–54. https://doi.org/10.1016/j.compmedimag.2018.05.004

    Article  Google Scholar 

  46. Rehman A, Khan MA, Mehmood Z, Saba T, Sardaraz M, Rashid M (2020 Apr) Microscopic melanoma detection and classification: a framework of pixel-based fusion and multilevel features reduction. Microsc Res Tech 83(4):410–423. https://doi.org/10.1002/jemt.23429

  47. Saba T, Khan MA, Rehman A, Marie-Sainte SL (2019) Region extraction and classification of skin cancer: A heterogeneous framework of deep CNN features fusion and reduction. J Med Syst:43

  48. Shahin AH, Kamal, A, Elattar, MA (2018) Deep ensemble learning for skin lesion classification from dermoscopic images. In: IEEE 9th Cairo international biomedical engineering conference - CIBEC’2018, pp 150–153. https://doi.org/10.1109/CIBEC.2018.8641815.

  49. Shahin AH, Kamal A, Elattar MA (2018) Deep ensemble learning for skin lesion classification from dermoscopic images. In: IEEE 9th Cairo international biomedical engineering conference - CIBEC’2018, pp 150–153. https://doi.org/10.1109/CIBEC.2018.8641815.

  50. Song L, Liu X, Chen S, Liu S, Liu X, Muhammad K, Bhattacharyya S (2022) A deep fuzzy model for diagnosis of COVID-19 from CT images. Appl Soft Comput 122:108883. https://doi.org/10.1016/j.asoc.2022.108883

    Article  Google Scholar 

  51. Song L, Liu X, Chen S, Liu S, Liu X, Muhammad K, Bhattacharyya S (2022) A deep fuzzy model for diagnosis of COVID-19 from CT images. Appl Soft Comput 122:108883. https://doi.org/10.1016/j.asoc.2022.108883

    Article  Google Scholar 

  52. Stergiou CL, Plageras AP, Psannis KE, Gupta BB (2020) Secure machine learning scenario from big data in cloud computing via internet of things network. In: Gupta B, Perez G, Agrawal D, Gupta D (eds) Handbook of computer networks and cyber security. Springer, Cham. https://doi.org/10.1007/978-3-030-22277-2_21

    Chapter  Google Scholar 

  53. Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, Bray F (2021 May) Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 71(3):209–249. https://doi.org/10.3322/caac.21660

  54. Tan M, Le QV (2019) EfficientNet: rethinking model scaling for convolutional neural networks. arXiv. https://doi.org/10.48550/arXiv.1905.11946

  55. Tomar NK, Jha D, Riegler MA, Johansen HD, Johansen D, Rittscher J, Halvorsen P, Ali S (2021). FANet: A Feedback Attention Network for Improved Biomedical Image Segmentation. http://arxiv.org/abs/2103.17235.

  56. Tschandl P, Rosendahl C, Kittler H (2018) The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions. Sci Data 5:180161. https://doi.org/10.1038/sdata.2018.161

    Article  Google Scholar 

  57. 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).

  58. Xie Y, Zhang J, Xia Y, Shen C (2020) A mutual bootstrapping model for automated skin lesion segmentation and classification. IEEE Trans Med Imaging 39(7):2482–2493

    Article  Google Scholar 

  59. Yuan Y, Lo YC (2019) Improving Dermoscopic image segmentation with enhanced convolutional-Deconvolutional networks. IEEE J Biomed Heal Informatics 23(2):519–526

    Article  MathSciNet  Google Scholar 

  60. Zagoruyko S, Komodakis N (2016) Paying more attention to attention: improving the performance of convolutional neural networks via attention transfer. arXiv. https://doi.org/10.48550/arXiv.1612.03928.

  61. Zhao P, Zhang J, Fang W, Deng S (2020) SCAU-net: Spatial-Channel attention U-net for gland segmentation. Frontiers in Bioengineering and Biotechnology 8. https://doi.org/10.3389/fbioe.2020.00670

  62. Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, Liang J (2018) UNet++: a nested u-net architecture for medical image segmentation. Lecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics). LNCS 11045:3–11. https://doi.org/10.1007/978-3-030-00889-5_1

    Article  Google Scholar 

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Acknowledgements

The authors would like to thank the Center of Excellence (CoE) for the Artificial Intelligence Lab at the National Institute of Technology Tiruchirappalli, Tamil Nadu, India, for providing the computational resources.

Funding

This research work was partly funded by the Scheme for Promotion of Academic and Research Collaboration (SPARC), Ministry of Education (MoE) Government of India under grant id SPARC-P641/2019.

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Authors and Affiliations

  1. Department of Electronics and Communication Engineering, National Institute of Technology Tiruchirappalli, Tiruchirappalli, Tamil Nadu, India

    Ravindranath Kadirappa, Deivalakshmi S. & Pandeeswari R.

  2. Department of Electrical and Computer, Biomedical Engineering, University of Saskatchewan, Saskatoon, Canada

    Seok-Bum Ko

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  1. Ravindranath Kadirappa
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Appendix A

Appendix A

Fig. 15
figure 15

Stem Layer of EfficientNet family of Architectures

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Fig. 16
figure 16

Basic building blocks (modules 1–5) of the EfficientNet architectures

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Fig. 17
figure 17

Subblocks of EfficientNet architectures

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Fig. 18
figure 18

EfficientNet B1 architecture. In Fig. 18, ×2 and ×3 indicates the blocks are repeated twice and thrice, respectively

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Kadirappa, R., S., D., R., P. et al. An automated multi-class skin lesion diagnosis by embedding local and global features of Dermoscopy images. Multimed Tools Appl 82, 34885–34912 (2023). https://doi.org/10.1007/s11042-023-14892-2

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