Skip to main content

Advertisement

Log in

CNN-Based Deep Learning Model for Early Identification and Categorization of Melanoma Skin Cancer Using Medical Imaging

  • Original Research
  • Published:
SN Computer Science Aims and scope Submit manuscript

Abstract

In human life, skin cancer is a curse. If not appropriately diagnosed, it spreads around all body parts in the earlier stage. The melanoma skin cancer death rate is 75% all over the world. There is an urgent need for a cure to be in place. Swarm and evolutionary computation for healthcare deals with real-world healthcare applications. One of the best ways to do this is to find the genes that need to be turned off such that only cancer cells can die. The current therapies used to cure cancer focus on turning off cancer cells but, at times, affect healthy tissues. Using the power of Artificial Intelligence, one can easily mark out the cancer-affected area and provide treatment accordingly. Segmentation techniques can be used to mark cancer lesions, and advanced deep learning algorithms in computer vision can help classify which class of cancer lesions. Automatic detection of melanoma by using a dermoscopic sample is tricky to find the stage and percentage of lesions affected by using deep convolutional neural networks with the help of machine vision tools. To predict skin lesions, we developed a model with three layers, each having output channels of 16, 32, and 64, respectively. For this research, different samples were collected from the international skin imaging collaboration database (ISIC2019, ISIC2020, ISIC2021). The study computed the most important parameters for classifying and identifying the lesion with accuracy, precision, recall, and specificity values. The proposed DCNN model classifier attained an accuracy of 88.82%, 93.45%, and 95.15% with the respective datasets of ISIC2019, ISIC2020, and ISIC2021, indicating high performance compared with the other state-of-the-art networks. The proposed model is a better framework for automating early detection and classification of melanoma skin cancer to protect many lives and avoid mishaps.

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

Access this article

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Algorithm 1
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15

Similar content being viewed by others

Data Availability

The dataset used for the findings will be shared by the corresponding author upon request.

References

  1. Ashraf R, Afzal S, Rehman AU, Gul S, Baber J, Bakhtyar M, Mehmood I, Song OYY, Maqsood M. Region-of-interest based transfer learning assisted framework for skin cancer detection. IEEE Access. 2020;8:147858–71.

    Article  Google Scholar 

  2. Agbley BL, Li JP, Haq AU, Bankas EK, Mawuli CB, Ahmad S, Khan S, Khan AR. Federated fusion of magnified histopathological images for breast tumor classification in the internet of medical things. IEEE J Biomed Health Inf. 2023;28(6):3389–400.

    Article  Google Scholar 

  3. Darapaneni N, et al. American sign language detection using instance based segmentation. In: 2021 IEEE international IOT, electronics and mechatronics conference (IEMTRONICS); 2021. pp. 1–6.

  4. Alryalat SA, Al-Antary M, Arafa Y, Azad B, Boldyreff C, Ghnaimat T, Al-Antary N, Alfegi S, Elfalah M, Abu-Ameerh M. Deep learning prediction of response to anti-VEGF among diabetic macular edema patients: treatment response analyzer system (TRAS). Diagnostics. 2022;12(2):312.

    Article  Google Scholar 

  5. Haq AU, Li JP, Khan I, Agbley BL, Ahmad S, Uddin MI, Zhou W, Khan S, Alam I. DEBCM: deep learning-based enhanced breast invasive ductal carcinoma classification model in IoMT healthcare systems. IEEE J Biomed Health Inf. 2022;3:1207–17.

    Google Scholar 

  6. Rajinikanth V, Kadry S, Damaševičius R, Sankaran D, Mohammed MA, Chander S. Skin melanoma segmentation using VGG-UNet with Adam/SGD Optimizer: a study. In: 2022 Third international conference on intelligent computing instrumentation and control technologies (ICICICT). IEEE; 2022. pp. 982–986.

  7. Nawaz M, Nazir T, Masood M, Ali F, Khan MA, Tariq U, Sahar N, Damaševičius R. Melanoma segmentation: a framework of improved DenseNet77 and UNET convolutional neural network. Int J Imaging Syst Technol. 2022;32(6):2137–53.

    Article  Google Scholar 

  8. Kassani SH, Kassani PH. A comparative study of deep learning architectures on melanoma detection. Tissue Cell. 2019;58:76–83.

    Article  Google Scholar 

  9. Yap MH, et al. Automated breast ultrasound lesions detection using convolutional neural networks. IEEE J Biomed Health Inform. 2018;22(4):1218–26. https://doi.org/10.1109/JBHI.2017.2731873.

    Article  Google Scholar 

  10. Khan MA, Sharif M, Akram T, Bukhari SAC, Nayak RS. Developed Newton-Raphson based deep features selection framework for skin lesion recognition. Pattern Recognit Lett. 2020;129:293–303.

    Article  Google Scholar 

  11. Janney BJ, Roslin SE, Shelcy MJ. A comparative analysis of skin cancer detection based on SVM, ANN and Naive Bayes classifier. In: 2018 International conference on recent innovations in electrical, electronics & communication engineering (ICRIEECE); 2018. pp. 1694–1699. https://doi.org/10.1109/ICRIEECE44171.2018.9008943.

  12. Sharma AK, et al. Dermatologist-level classification of skin cancer using cascaded ensembling of convolutional neural network and handcrafted features based deep neural network. IEEE Access. 2022;10:17920–32. https://doi.org/10.1109/ACCESS.2022.3149824.

    Article  Google Scholar 

  13. Yu Z, et al. Early melanoma diagnosis with sequential dermoscopic images. IEEE Trans Med Imaging. 2022;41(3):633–46. https://doi.org/10.1109/TMI.2021.3120091.

    Article  Google Scholar 

  14. Putra TA, Rufaida SI, Leu J-S. Enhanced skin condition prediction through machine learning using dynamic training and testing augmentation. IEEE Access. 2020;8:40536–46. https://doi.org/10.1109/ACCESS.2020.2976045.

    Article  Google Scholar 

  15. Medi PR, Nemani P, Pitta VR, Udutalapally V, Das D, Mohanty SP. SkinAid: a GAN-based automatic skin lesion monitoring method for IoMT frameworks. In: 2021 19th OITS international conference on information technology (OCIT); 2021. pp. 200–205. https://doi.org/10.1109/OCIT53463.2021.00048.

  16. Das S, Das D. Skin lesion segmentation and classification: a deep learning and Markovian approach. In: 2021 IEEE Mysuru (Mysore) sub section international conference (MysuruCon); 2021. pp. 546–551. https://doi.org/10.1109/MysuruCon52639.2021.9641583.

  17. Rocholl M, Hannappel J, Ludewig M, John SM. UV-induced skin cancer knowledge, sun exposure, and tanning behavior among university students: investigation of an opportunity sample of German University Students. J Skin Cancer. 2021. https://doi.org/10.1155/2021/5558694.

    Article  Google Scholar 

  18. Baumann BC, MacArthur KM, Brewer JD, et al. Management of primary skin cancer during a pandemic: multidisciplinary recommendations. Cancer. 2020;126(17):3900–6. https://doi.org/10.1002/cncr.32969.

    Article  Google Scholar 

  19. Hao S, Cui Y, Wang J. Segmentation scale effect analysis in the object-oriented method of high-spatial-resolution image classification. Sensors. 2021;21(23):7935.

    Article  Google Scholar 

  20. Azad R, Rouhier L, Cohen-Adad J. Stacked hourglass network with a multi-level attention mechanism: Where to look for intervertebral disc labeling. In: Proc. Int. Workshop Mach. Learn. Med. Imag. Cham, Switzerland: Springer; 2021. pp. 406–415.

  21. Bılgıç B. Comparison of breast cancer and skin cancer diagnoses using deep learning method. In: 2021 29th signal processing and communications applications conference (SIU); 2021. pp. 1–4. https://doi.org/10.1109/SIU53274.2021.9477992

  22. Jusman Y, Firdiantika IM, Dharmawan DA, Purwanto K. Performance of Multi-Layer Perceptron and Deep Neural Networks in Skin Cancer Classification. In: 2021 IEEE 3rd global conference on life sciences and technologies (LifeTech); 2021. pp. 534–538. https://doi.org/10.1109/LifeTech52111.2021.9391876.

  23. Younis H, Bhatti MH, Azeem M. Classification of skin cancer dermoscopy images using transfer learning. In: 2019 15th International conference on emerging technologies (ICET); 2019. pp. 1–4. https://doi.org/10.1109/ICET48972.2019.8994508.

  24. Sigurdsson S, Philipsen PA, Hansen LK, Larsen J, Gniadecka M, Wulf HC. Detection of skin cancer by classification of Raman spectra. IEEE Trans Biomed Eng. 2004;51(10):1784–93. https://doi.org/10.1109/TBME.2004.831538.

    Article  Google Scholar 

  25. Satheesha TY, Satyanarayana D, Prasad MNG, Dhruve KD. Melanoma is skin deep: a 3D reconstruction technique for computerized dermoscopic skin lesion classification. IEEE J Transl Eng Health Med. 2017;5:1–17. https://doi.org/10.1109/JTEHM.2017.2648797.

    Article  Google Scholar 

  26. Pham T-C, Doucet A, Luong C-M, Tran C-T, Hoang V-D. Improving skin-disease classification based on customized loss function combined with balanced mini-batch logic and real-time image augmentation. IEEE Access. 2020;8:150725–37. https://doi.org/10.1109/ACCESS.2020.3016653.

    Article  Google Scholar 

  27. Wei L, Ding K, Hu H. Automatic skin cancer detection in dermoscopy images based on ensemble lightweight deep learning network. IEEE Access. 2020;8:99633–47. https://doi.org/10.1109/ACCESS.2020.2997710.

    Article  Google Scholar 

  28. Thurnhofer-Hemsi K, López-Rubio E, Domínguez E, Elizondo DA. Skin lesion classification by ensembles of deep convolutional networks and regularly spaced shifting. IEEE Access. 2021;9:112193–205. https://doi.org/10.1109/ACCESS.2021.3103410.

    Article  Google Scholar 

  29. Khan MQ, et al. Classification of Melanoma and Nevus in Digital Images for Diagnosis of Skin Cancer. IEEE Access. 2019;7:90132–44. https://doi.org/10.1109/ACCESS.2019.2926837.

    Article  Google Scholar 

  30. Gavrilov DA, Melerzanov AV, Shchelkunov NN, Zakirov EI. ‘Use of neural network-based deep learning techniques for the diagnostics of skin diseases.’ Biomed Eng. 2019;52(5):348–52.

    Article  Google Scholar 

  31. El-Khatib H, Popescu D, Ichim L. Deep learning-based methods for automatic diagnosis of skin lesions. Sensors. 2020;20(6):1753.

    Article  Google Scholar 

  32. Gu Y, Ge Z, Bonnington CP, Zhou J. Progressive transfer learning and adversarial domain adaptation for cross-domain skin disease classification. IEEE J Biomed Health Inform. 2020;24(5):1379–93. https://doi.org/10.1109/JBHI.2019.2942429.

    Article  Google Scholar 

  33. Naeem A, Farooq MS, Khelifi A, Abid A. Malignant melanoma classification using deep learning: datasets, performance measurements, challenges and opportunities. IEEE Access. 2020;8:110575–97. https://doi.org/10.1109/ACCESS.2020.3001507.

    Article  Google Scholar 

  34. Yao P, et al. Single model deep learning on imbalanced small datasets for skin lesion classification. IEEE Trans Med Imaging. 2022;41(5):1242–54. https://doi.org/10.1109/TMI.2021.3136682.

    Article  MathSciNet  Google Scholar 

  35. Mukherjee S, Adhikari A, Roy M. Malignant melanoma classification using cross-platform dataset with deep learning CNN architecture. In: Recent trends in signal and image processing. Singapore: Springer; 2019. pp. 31–41.

  36. Albert BA. Deep learning from limited training data: novel segmentation and ensemble algorithms applied to automatic melanoma diagnosis. IEEE Access. 2020;8:31254–69. https://doi.org/10.1109/ACCESS.2020.2973188.

    Article  Google Scholar 

  37. Celebi ME, Codella N, Halpern A, Shen D. Guest editorial skin lesion image analysis for melanoma detection. IEEE J Biomed Health Inform. 2019;23(2):479–80. https://doi.org/10.1109/JBHI.2019.2897338.

    Article  Google Scholar 

  38. Ananth C, Therese MJ. A survey on melanoma: skin cancer through computerized diagnosis; 2020. Available at SSRN https://ssrn.com/abstract=3551811 or https://doi.org/10.2139/ssrn.3551811.

  39. Asai Y, Nguyen P, Hanna TP. Impact of the COVID-19 pandemic on skin cancer diagnosis: a population-based study. PLoS ONE. 2021;16(3): e0248492. https://doi.org/10.1371/journal.pone.0248492.

    Article  Google Scholar 

  40. Seretis K, Boptsi E, Boptsi A, et al. The impact of treatment delay on skin cancer in COVID-19 era: a case–control study. World J SurgOnc. 2021;19:350. https://doi.org/10.1186/s12957-021-02468-z.

    Article  Google Scholar 

  41. Abayomi-Alli OO, Damasevicius R, Misra S, Maskeliunas R, Abayomi-Alli A. Malignant skin melanoma detection using image augmentation by oversamplingin nonlinear lower-dimensional embedding manifold. Turk J Electr Eng Comput Sci. 2021;29(8):2600–14.

    Article  Google Scholar 

  42. Dascalu A, Walker BN, Oron Y, et al. Non-melanoma skin cancer diagnosis: a comparison between dermoscopic and smartphone images by unified visual and sonification deep learning algorithms. J Cancer Res ClinOncol. 2021. https://doi.org/10.1007/s00432-021-03809-x.

    Article  Google Scholar 

  43. Kann BH, Hicks DF, Payabvash S, Mahajan A, Du J, Gupta V, Park HS, et al. Multi-institutional validation of deep learning for pretreatment identification of extranodal extension in head and neck squamous cell carcinoma. J Clin Oncol. 2020;38(12):1304–11. https://doi.org/10.1200/JCO.19.02031.

    Article  Google Scholar 

Download references

Acknowledgements

We thank the Deanship of Scientific Research, Prince Sattam Bin Abdulaziz University, Alkharj, Saudi Arabia, for help and support. This study is supported via funding from Prince Sattam Bin Abdulaziz University project number (PSAU/2024/R/1445).

Funding

This study is supported via funding from Prince Sattam Bin Abdulaziz University project number (PSAU/2024/R/1445).

Author information

Authors and Affiliations

Authors

Contributions

All authors contributed to design and development of the system as well as the manuscript. All authors have read and approved the final manuscript.

Corresponding author

Correspondence to Sultan Ahmad.

Ethics declarations

Conflict of Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Ethical Approval

This article does not contain any studies with human participants performed by any of the authors.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Pandimurugan, V., Ahmad, S., Prabu, A.V. et al. CNN-Based Deep Learning Model for Early Identification and Categorization of Melanoma Skin Cancer Using Medical Imaging. SN COMPUT. SCI. 5, 911 (2024). https://doi.org/10.1007/s42979-024-03270-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s42979-024-03270-w

Keywords

Navigation