Hybrid Deep Learning Model for Skin Cancer Detection Based on CNN and LSTM
Keywords:
Skin cancerSkin cancer, Melanoma, Dermoscopy, Convolutional neural, network, Long short-term memory, Hybrid deep learning, Survey
Abstract
Skin cancer is among the most frequently diagnosed malignancies
worldwide, and its outcome depends strongly on how early
melanoma and related lesions are recognised. Convolutional neural networks
(CNNs) have reached specialist-level accuracy on dermoscopic images,
yet they treat each image as a single static input and say little about
how local lesion structures relate to one another. Hybrid models that
couple a CNN feature extractor with a long short-term memory (LSTM)
network have therefore attracted growing interest, because the recurrent
stage can model dependencies across image patches, feature-map regions,
multiple scales or successive examinations of the same lesion. This survey
reviews CNN–LSTM hybrid architectures for skin cancer detection
and classification published between 2017 and 2026. It summarises the
clinical background and public dermoscopy benchmarks, explains the
computational principles of the CNN and LSTM components, and proposes
a taxonomy of hybrid designs according to how the input sequence
is constructed and how the recurrent unit is configured (unidirectional,
bidirectional or attention-augmented). Representative studies are compared
in terms of backbone, recurrent unit, dataset and reported performance,
and the comparison is used to identify recurring weaknesses:
class imbalance, limited diversity of skin tones, weak justification of the
“sequence” in single-image settings, inconsistent reporting and limited
interpretability. The review concludes with research directions, including
longitudinal lesion monitoring, transformer–recurrent hybrids, multimodal
fusion with patient metadata, federated training and lightweight
deployment at the point of care.
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Reports 15, 24994 (2025). https://doi.org/10.1038/s41598-025-08954-8
2. Adegun, A., Viriri, S.: Deep learning techniques for skin lesion analysis and
melanoma cancer detection: A survey of state-of-the-art. Artificial Intelligence
Review 54, 811–841 (2021). https://doi.org/10.1007/s10462-020-09865-y
3. Agarwal, A.K., Hamid, A.B.B.A., Ather, D., Tiwari, R.G., De, I., Krishna,
K.R.: Lesion-aware ordinal transformer for diabetic retinopathy classification from
fundus images. Biomedical and Pharmacology Journal 19(1), 233–245 (2026).
https://doi.org/10.13005/bpj/3349
4. Ather, D., Yusupov, I., Duggal, S., Kumar, R., Sagar, P., Jain, V.: Advancements
in deep learning for early detection and diagnosis across multiple disease domains.
In: Proc. 4th International Conference on Technological Advancements in Computational
Sciences (ICTACS), Tashkent, Uzbekistan. pp. 1460–1464. IEEE (2024).
https://doi.org/10.1109/ICTACS62700.2024.10840864
5. Brinker, T.J., et al.: Skin cancer classification using convolutional neural networks:
Systematic review. Journal of Medical Internet Research 20(10), e11936 (2018).
https://doi.org/10.2196/11936
6. Cho, K., et al.: Learning phrase representations using RNN encoder–decoder for
statistical machine translation. In: Proc. Conference on Empirical Methods in
Natural Language Processing (EMNLP). pp. 1724–1734 (2014). https://doi.org/
10.3115/v1/D14-1179
7. Codella, N.C.F., et al.: 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: Proc. IEEE 15th International
Symposium on Biomedical Imaging (ISBI). pp. 168–172. IEEE (2018).
https://doi.org/10.1109/ISBI.2018.8363547
8. Combalia, M., et al.: BCN20000: Dermoscopic lesions in the wild.
arXiv:1908.02288 (2019)
9. Daneshjou, R., et al.: Disparities in dermatology AI performance on a diverse,
curated clinical image set. Science Advances 8(32), eabq6147 (2022). https://
doi.org/10.1126/sciadv.abq6147
10. Donahue, J., et al.: Long-term recurrent convolutional networks for visual recognition
and description. In: Proc. IEEE Conference on Computer Vision and Pattern
Recognition (CVPR). pp. 2625–2634. IEEE (2015). https://doi.org/10.1109/
CVPR.2015.7298878
11. Dosovitskiy, A., et al.: An image is worth 16×16 words: Transformers for image
recognition at scale. In: Proc. International Conference on Learning Representations
(ICLR) (2021). arXiv:2010.11929
12. Esteva, A., Kuprel, B., Novoa, R.A., Ko, J., Swetter, S.M., Blau, H.M., Thrun, S.:
Dermatologist-level classification of skin cancer with deep neural networks. Nature
542, 115–118 (2017). https://doi.org/10.1038/nature21056
13. Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional
LSTM and other neural network architectures. Neural Networks 18(5–6), 602–610
(2005). https://doi.org/10.1016/j.neunet.2005.06.042
14. Gupta, S., Abdul Hamid, A.B., Nyamasvisva, T.E., Jain, V., Tyagi, N., Mun, N.K.,
Ather, D.: Cotton disease prediction using deep transfer learning: Comparative
analysis of ResNet50, VGG16 and InceptionV3 models. International Journal ofEngineering, Science and Information Technology 5(3), 588–594 (2025). https:
//doi.org/10.52088/ijesty.v5i3.1116
15. Haenssle, H.A., et al.: Man against machine: Diagnostic performance of a deep
learning convolutional neural network for dermoscopic melanoma recognition in
comparison to 58 dermatologists. Annals of Oncology 29(8), 1836–1842 (2018).
https://doi.org/10.1093/annonc/mdy166
16. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition.
In: Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
pp. 770–778. IEEE (2016). https://doi.org/10.1109/CVPR.2016.90
17. Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Computation
9(8), 1735–1780 (1997). https://doi.org/10.1162/neco.1997.9.8.1735
18. Hussein, A.A., Montaser, A.M., Elsayed, H.A.: Skin cancer image classification
using hybrid quantum deep learning model with BiLSTM and MobileNetV2.
Quantum Machine Intelligence 7(2), 66 (2025). https://doi.org/10.
1007/s42484-025-00288-y
19. Kawahara, J., Daneshvar, S., Argenziano, G., Hamarneh, G.: Seven-point checklist
and skin lesion classification using multitask multimodal neural nets. IEEE Journal
of Biomedical and Health Informatics 23(2), 538–546 (2019). https://doi.org/
10.1109/JBHI.2018.2824327
20. Lin, T.-Y., Goyal, P., Girshick, R., He, K., Doll´ar, P.: Focal loss for dense object
detection. In: Proc. IEEE International Conference on Computer Vision (ICCV).
pp. 2980–2988. IEEE (2017). https://doi.org/10.1109/ICCV.2017.324
21. Mahajan, S.T., Dashore, P.: Hybrid deep learning models for skin lesion classification:
A comparative review and future directions. EPJ Web of Conferences 341,
01051 (2025). https://doi.org/10.1051/epjconf/202534101051
22. Mateen, M., Hayat, S., Arshad, F., Gu, Y.-H., Al-antari, M.A.: Hybrid deep learning
framework for melanoma diagnosis using dermoscopic medical images. Diagnostics
14(19), 2242 (2024). https://doi.org/10.3390/diagnostics14192242
23. Mendon¸ca, T., Ferreira, P.M., Marques, J.S., Marcal, A.R.S., Rozeira, J.: PH2
– A dermoscopic image database for research and benchmarking. In: Proc. 35th
Annual International Conference of the IEEE Engineering in Medicine and Biology
Society (EMBC). pp. 5437–5440. IEEE (2013). https://doi.org/10.1109/EMBC.
2013.6610779
24. Naeem, A., Anees, T., Khalil, M., Zahra, K., Naqvi, R.A., Lee, S.-W.: SNC Net:
Skin cancer detection by integrating handcrafted and deep learning-based features
using dermoscopy images. Mathematics 12(7), 1030 (2024). https://doi.org/10.
3390/math12071030
25. Naitik, Raj, D., Rajan, D.K., Gupta, A.K., Agrawal, A.K., Krishna, K.R.: Enhancing
toxic comment detection with BiLSTM-based deep learning model. In: Proc.
International Conference on Information Science and Communications Technologies
(ICISCT), Seoul, Korea. pp. 206–211. IEEE (2024). https://doi.org/10.
1109/ICISCT64202.2024.10956568
26. Pathak, S., Quraishi, S.J., Singh, A., Singh, M., Arora, K., Ather, D.: A comparative
analysis of machine learning models: SVM, na¨ıve Bayes, random forest, and
LSTM in predictive analytics. In: Proc. 3rd International Conference on Technological
Advancements in Computational Sciences (ICTACS). pp. 790–795. IEEE
(2023). https://doi.org/10.1109/ICTACS59847.2023.10390255
27. Pranjal, Vaishnavi, Divyansh, Raj, D., Jain, V., Agarwal, A.K.: Adversarial attacks
on neural networks. In: Cyber Intelligence and Information Retrieval (CIIR
2023). LNNS, vol. 1025. Springer, Singapore (2024). https://doi.org/10.1007/978-981-97-3594-5_34
28. Ravikanth, V., Jahnavi, D., Hemanth, V.R., Reddy, J.J., Kumar, D.V.H.: Skin-
Wise: Hybrid CNN-LSTM for cancer diagnosis. In: Evolution in Signal Processing
and Telecommunication Networks (ICMEET 2025). LNEE, vol. 1601, pp. 205–211.
Springer, Cham (2026). https://doi.org/10.1007/978-3-032-20544-5_18
29. Rieke, N., et al.: The future of digital health with federated learning. npj Digital
Medicine 3, 119 (2020). https://doi.org/10.1038/s41746-020-00323-1
30. Rotemberg, V., et al.: A patient-centric dataset of images and metadata for identifying
melanomas using clinical context. Scientific Data 8, 34 (2021). https:
//doi.org/10.1038/s41597-021-00815-z
31. Rupakula, T., Murugadoss, R., Karthikeyan, A., Aruna, M., Periyasamy, P.R.,
Krishnan, K.: Hybrid intelligence system CNN-LSTM architecture for accurate
skin cancer classification using dermoscopic image analysis. Genetics and Molecular
Research 25(9s) (2026). https://doi.org/10.4238/c4mb4z83
32. Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-
CAM: Visual explanations from deep networks via gradient-based localization. In:
Proc. IEEE International Conference on Computer Vision (ICCV). pp. 618–626.
IEEE (2017). https://doi.org/10.1109/ICCV.2017.74
33. Shaik, A., Dutta, S.S., Sawant, I.M., Kumar, S., Balasundaram, A., De, K.:
An attention based hybrid approach using CNN and BiLSTM for improved
skin lesion classification. Scientific Reports 15 (2025). https://doi.org/10.1038/
s41598-025-00025-2
34. Shi, X., Chen, Z., Wang, H., Yeung, D.-Y., Wong, W.-K., Woo, W.-C.: Convolutional
LSTM network: A machine learning approach for precipitation nowcasting.
In: Advances in Neural Information Processing Systems (NeurIPS), vol. 28, pp.
802–810 (2015)
35. Shukla, M.M., Tripathi, B.K., Dwivedi, T., Tripathi, A., Chaurasia, B.K.: A
hybrid CNN with transfer learning for skin cancer disease detection. Medical &
Biological Engineering & Computing 62(10), 3057–3071 (2024). https://doi.org/
10.1007/s11517-024-03115-x
36. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale
image recognition. In: Proc. International Conference on Learning Representations
(ICLR) (2015). arXiv:1409.1556
37. Singh, S., Prakash, P., Baghel, G., Singh, A., Raj, D., Agrawal, A.K.: Banana
crop health: A deep learning-based model for disease detection and classification.
In: Proc. 27th International Symposium on Wireless Personal Multimedia
Communications (WPMC), Greater Noida, India. pp. 1–6. IEEE (2024).
https://doi.org/10.1109/WPMC63271.2024.10863138
38. Singhal, P., Kaushik, M., Mansoori, A., Agrawal, A.K., Raj, D.: Transformerassisted
deep learning model for medical image enhancement. In: Proc. 13th International
Conference on Computing for Sustainable Global Development (INDIACom),
New Delhi, India. pp. 1–7. IEEE (2026). https://doi.org/10.23919/
INDIACom70271.2026.11526310
39. Sung, H., Ferlay, J., Siegel, R.L., Laversanne, M., Soerjomataram, I., Jemal, A.,
Bray, F.: Global cancer statistics 2020: GLOBOCAN estimates of incidence and
mortality worldwide for 36 cancers in 185 countries. CA: A Cancer Journal for
Clinicians 71(3), 209–249 (2021). https://doi.org/10.3322/caac.21660
40. Tan, M., Le, Q.V.: EfficientNet: Rethinking model scaling for convolutional neural
networks. In: Proc. 36th International Conference on Machine Learning (ICML).
PMLR, vol. 97, pp. 6105–6114 (2019)
41. Tripathi, R.P., Khatri, S.K., Van Greunen, D., Ather, D.: Enhancing breast cancer
diagnosis through segmentation-driven generative adversarial networks for synthetic
mammogram generation. In: Proc. 3rd International Conference on Technological
Advancements in Computational Sciences (ICTACS). pp. 1078–1082. IEEE
(2023). https://doi.org/10.1109/ICTACS59847.2023.10389935
42. Tschandl, P., Rosendahl, C., Kittler, H.: The HAM10000 dataset, a large collection
of multi-source dermatoscopic images of common pigmented skin lesions. Scientific
Data 5, 180161 (2018). https://doi.org/10.1038/sdata.2018.161
43. Walia, S., Singh, S., Mor, J., Raj, D., Jain, V., Krishna, K.R.: An efficient Caffe
model based multiple object detection and recognition system using R-CNN. In:
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Published
2026-10-29
How to Cite
Rashmi Sinha, & Anil Kumar Sagar. (2026). Hybrid Deep Learning Model for Skin Cancer Detection Based on CNN and LSTM. MATRIX Academic International Online Journal Of Engineering And Technology, 9(2), 43-56. Retrieved from https://maiojet.com/index.php/matrix/article/view/120
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Articles
