Automated skin cancer diagnosis using deep learning: a systematic review of state-of-the-art architectures, techniques and performance evaluation

Subaidabeevi Shafeena; Ramayyan Sumathy Vinod Kumar; Sikamony Sumathi Kumar; David Shahi

doi:10.35784/iapgos.7427

PDF (angielski)

Opublikowane: 2026-03-30

DOI: https://doi.org/10.35784/iapgos.7427

Numer Tom 16 Nr 1 (2026)

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Archiwum

2026

Tom 16 Nr 2
2026-06-30 27
Tom 16 Nr 1
2026-03-30 27

2025

Tom 15 Nr 4
2025-12-20 27
Tom 15 Nr 3
2025-09-30 24
Tom 15 Nr 2
2025-06-27 24
Tom 15 Nr 1
2025-03-31 26

2024

Tom 14 Nr 4
2024-12-21 25
Tom 14 Nr 3
2024-09-30 24
Tom 14 Nr 2
2024-06-30 24
Tom 14 Nr 1
2024-03-31 23

2023

Tom 13 Nr 4
2023-12-20 24
Tom 13 Nr 3
2023-09-30 25
Tom 13 Nr 2
2023-06-30 14
Tom 13 Nr 1
2023-03-31 12

2022

Tom 12 Nr 4
2022-12-30 16
Tom 12 Nr 3
2022-09-30 15
Tom 12 Nr 2
2022-06-30 16
Tom 12 Nr 1
2022-03-31 9

Authors

Subaidabeevi Shafeena

inform2shafeena@gmail.com

Noorul Islam Centre for Higher Education

https://orcid.org/0009-0008-1752-4720

Ramayyan Sumathy Vinod Kumar

rsvinodkumar69@yahoo.co.in

Noorul Islam Centre for Higher Education

Sikamony Sumathi Kumar

kumarss@live.com

Noorul Islam Centre for Higher Education

David Shahi

shahijulian@gmail.com

Noorul Islam Centre for Higher Education

Abstrakt

Niniejszy przegląd literatury przedstawia kompleksową analizę technik głębokiego uczenia stosowanych w diagnostyce raka skóry. Wczesne rozpoznanie ma kluczowe znaczenie dla poprawy przeżywalności pacjentów, a głębokie uczenie wykazuje obiecujące wyniki. Artykuł omawia podstawy raka skóry, różne architektury sieci neuronowych oraz ich skuteczność klasyfikacyjną. Analizowane jest zastosowanie modeli głębokiego uczenia w procesie podejmowania decyzji klinicznych oraz ocena rzeczywistych zbiorów danych wykorzystywanych do testowania technik wykrywania raka skóry. Przedstawiono strategie treningowe służące poprawie jakości modeli głębokiego uczenia. W pracy oceniono kluczowe wskaźniki efektywności, takie jak dokładność, precyzja, czułość oraz wskaźnik F1. Przegląd ten podkreśla rosnące znaczenie głębokiego uczenia w diagnostyce raka skóry, ukazując jego potencjał w poprawie usług dla pacjentów i rozwoju praktyki klinicznej.

Słowa kluczowe:

konwolucyjne sieci neuronowe, klasyfikacja, czerniak, obrazowanie medyczne

Bibliografia

[1] Abdulredah, A. A., Fadhel, M. A., Alzubaidi, L., Duan, Y., Kherallah, M., & Charfi, F. (2025). Towards unbiased skin cancer classification using deep feature fusion. BMC Medical Informatics and Decision Making, 25(1), 48. https://doi.org/10.1186/s12911-025-02889-w

[2] Akaike, T., & Nghiem, P. (2021). Scientific and clinical developments in Merkel cell carcinoma: A polyomavirus-driven, often-lethal skin cancer. Journal of Dermatological Science, 105(1), 2–10. https://doi.org/10.1016/j.jdermsci.2021.10.004

[3] Akinrinade, O., & Du, C. (2024). Skin cancer detection using deep machine learning techniques. Intelligence-Based Medicine, 11, 100191. https://doi.org/10.1016/j.ibmed.2024.100191

[4] Alanazi, S. A. (2022). Melanoma identification through x-ray modality using inception-V3 based convolutional neural network. Computers, Materials & Continua/Computers, Materials & Continua (Print), 72(1), 37–55. https://doi.org/10.32604/cmc.2022.020118

[5] Arulpandy, P., & M, T. P. (2020). Speckle noise reduction and image segmentation based on a modified mean filter. SHILAP Revista De Lepidopterología, 27(4), 221–239. https://doi.org/10.24423/cames.290

[6] Ashraf, R., Afzal, S., Rehman, A. U., Gul, S., Baber, J., Bakhtyar, M., Mehmood, I., Song, O., & Maqsood, M. (2020). Region-of-Interest based transfer Learning Assisted framework for skin cancer detection. IEEE Access, 8, 147858–147871. https://doi.org/10.1109/access.2020.3014701

[7] Attia, M., Hossny, M., Nahavandi, S., & Yazdabadi, A. (2017). Skin melanoma segmentation using recurrent and convolutional neural networks. Proceedings of the 14th IEEE International Symposium on Biomedical Imaging, 292–296. https://doi.org/10.1109/isbi.2017.7950522

[8] Baig, A. R., Abbas, Q., Almakki, R., Ibrahim, M. E. A., AlSuwaidan, L., & Ahmed, A. E. S. (2023b). Light-Dermo: a lightweight pretrained convolution neural network for the diagnosis of multiclass skin lesions. Diagnostics, 13(3), 385. https://doi.org/10.3390/diagnostics13030385

[9] Bazgir, E., Haque, E., Maniruzzaman, M., & Hoque, R. (2024). Skin cancer classification using Inception Network. World Journal of Advanced Research and Reviews, 21(2), 839–849. https://doi.org/10.30574/wjarr.2024.21.2.0500

[10] Budhiman, A., Suyanto, S., & Arifianto, A. (2019). Melanoma Cancer Classification Using ResNet with Data Augmentation. 2019 International Seminar on Research of Information Technology and Intelligent Systems (ISRITI), 17–20. https://doi.org/10.1109/isriti48646.2019.9034624

[11] Bushra, R. H., et al. (2022). Performance analysis of machine learning algorithms for malware classification (Doctoral dissertation, Brac University), http://hdl.handle.net/10361/21825

[12] Chaturvedi, S. S., Tembhurne, J. V., & Diwan, T. (2020). A multi-class skin Cancer classification using deep convolutional neural networks. Multimedia Tools and Applications, 79(39–40), 28477–28498. https://doi.org/10.1007/s11042-020-09388-2

[13] Chiou, A., Omiye, J. A., Gui, H., Swetter, S. M., Ko, J. M., Gastman, B., Arbesman, J., Cai, Z. R., Gevaert, O., Sadee, C., Rotemberg, V. M., Han, S. S., Tschandl, P., Dickman, M., Bailey, E., Bae, G., Bailin, P., Boldrick, J., Yekrang, K., & Caroline, P. (2024). MRA-MIDAS: Multimodal image dataset for AI-based skin cancer [Dataset]. In Stanford Center for Artificial Intelligence in Medicine and Imaging. https://doi.org/10.71718/15nz-jv40

[14] Clarke, P. (2019). Benign pigmented skin lesions. Australian Journal of General Practice, 48(6), 364–367. https://doi.org/10.31128/ajgp-12-18-4802

[15] Codella, N. C. F., Gutman, D., Celebi, M. E., Helba, B., Marchetti, M. A., Dusza, S. W., 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). Proceedings of the IEEE International Symposium on Biomedical Imaging. 168–172. https://doi.org/10.1109/isbi.2018.8363547

[16] Corona, R., Mele, A., Amini, M., De Rosa, G., Coppola, G., Piccardi, P., Fucci, M., Pasquini, P., & Faraggiana, T. (1996). Interobserver variability on the histopathologic diagnosis of cutaneous melanoma and other pigmented skin lesions. Journal of Clinical Oncology, 14(4), 1218–1223. https://doi.org/10.1200/jco.1996.14.4.1218

[17] Devaraj, G. P., & Ravi, R. (2024). Advancing skin cancer diagnosis with a multi‐branch ShuffleNet architecture. International Journal of Imaging Systems and Technology, 34(2). https://doi.org/10.1002/ima.23051

[18] DeVries, T. & Ramachandran, D. (2017). Skin lesion classification using deep multi-scale convolutional neural networks. arXiv preprint. https://arxiv.org/abs/1703.01402

[19] Dhibar, S. (2024). ResNet101 and DAE for enhance quality and classification accuracy in skin cancer imaging. arXiv preprint. https://arxiv.org/abs/2403.14248

[20] Dinnes, J., Deeks, J. J., Chuchu, N., Di Ruffano, L. F., Matin, R. N., Thomson, D. R., Wong, K. Y., Aldridge, R. B., Abbott, R., Fawzy, M., Bayliss, S. E., Grainge, M. J., Takwoingi, Y., Davenport, C., Godfrey, K., Walter, F. M., Williams, H. C., & Group, C. S. C. D. T. A. (2018). Dermoscopy, with and without visual inspection, for diagnosing melanoma in adults. Cochrane Database of Systematic Reviews, 12(12), CD011902. https://doi.org/10.1002/14651858.cd011902.pub2

[21] Divya, D., & Ganeshbabu, T. R. (2020). Fitness adaptive deer hunting‐based region growing and recurrent neural network for melanoma skin cancer detection. International Journal of Imaging Systems and Technology, 30(3), 731–752. https://doi.org/10.1002/ima.22414

[22] Dowell-Esquivel, C., Lee, R., DiCaprio, R. C., & Nouri, K. (2023). Sebaceous carcinoma: an updated review of pathogenesis, diagnosis, and treatment options. Archives of Dermatological Research, 316(1), 55. https://doi.org/10.1007/s00403-023-02747-7

[23] Elgamal, M. (2013). Automatic skin cancer images classification. International Journal of Advanced Computer Science and Applications, 4(3). https://doi.org/10.14569/ijacsa.2013.040342

[24] Elshahawy, M., Elnemr, A., Oproescu, M., Schiopu, A., Elgarayhi, A., Elmogy, M. M., & Sallah, M. (2023). Early melanoma detection based on a hybrid YOLOV5 and ResNet technique. Diagnostics, 13(17), 2804. https://doi.org/10.3390/diagnostics13172804

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

[26] Fu’adah, Y. N., Pratiwi, N. C., Pramudito, M. A., & Ibrahim, N. (2020). Convolutional Neural Network (CNN) for automatic skin cancer Classification system. IOP Conference Series Materials Science and Engineering, 982(1), 012005. https://doi.org/10.1088/1757-899x/982/1/012005

[27] Flucke, U., Requena, L., & Mentzel, T. (2013). Radiation-induced vascular lesions of the skin. Advances in Anatomic Pathology, 20(6), 407–415. https://doi.org/10.1097/pap.0b013e3182a92e19

[28] Gangadevi, E., Lawanyashri, M., Dhanaraj, R. K., Shanmugam, S. K., Balusamy, B., & Santhi, K. (2024). Detecting skin cancer disease using LSTM (RNN) based on a modified electromagnetic field optimization algorithm. In Lecture notes in networks and systems (pp. 143–153). https://doi.org/10.1007/978-981-97-2671-4_11

[29] Geller, A. C., Swetter, S. M., Brooks, K., Demierre, M., & Yaroch, A. L. (2007). Screening, early detection, and trends for melanoma: Current status (2000-2006) and future directions. Journal of the American Academy of Dermatology, 57(4), 555–572. https://doi.org/10.1016/j.jaad.2007.06.032

[30] Girdhar, N., Sinha, A., & Gupta, S. (2022). RETRACTED ARTICLE: DenseNet-II: an improved deep convolutional neural network for melanoma cancer detection. Soft Computing, 27(18), 13285–13304. https://doi.org/10.1007/s00500-022-07406-z

[31] Gong, X., & Xiao, Y. (2021). A skin cancer detection interactive application based on CNN and NLP. Journal of Physics Conference Series, 2078(1), 012036. https://doi.org/10.1088/1742-6596/2078/1/012036

[32] Gupta, A., Thakur, S., & Rana, A. (2020). Study of Melanoma Detection and Classification Techniques. Proceedings of the 8th International Conference on Reliability, Infocom Technologies and Optimization (ICRITO), 1345–1350. https://doi.org/10.1109/icrito48877.2020.9197820

[33] Gururaj, H. L., Manju, N., Nagarjun, A., Aradhya, V. N. M., & Flammini, F. (2023). DeepSkin: A deep learning approach for skin cancer classification. IEEE Access, 11, 50205–50214. https://doi.org/10.1109/access.2023.3274848

[34] Haenssle, H., Fink, C., Schneiderbauer, R., Toberer, F., Buhl, T., Blum, A., Kalloo, A., Hassen, A. B. H., Thomas, L., Enk, A., Uhlmann, L., Alt, C., Arenbergerova, M., Bakos, R., Baltzer, A., Bertlich, I., Blum, A., Bokor-Billmann, T., Bowling, J., Braghiroli, N., Braun, R., Buder-Bakhaya, K., Buhl, T., Cabo, H., Cabrijan, L., Cevic, N., Classen, A., Deltgen, D., Fink, C., Georgieva, I., Hakim-Meibodi, LE., Hanner, S., Hartmann, F., Hartmann, J., Haus, G., Hoxha, E., Karls, R., Koga, H., Kreusch, J., Lallas, A., Majenka, P., Marghoob, A., Massone, C., Mekokishvili, L., Mestel, D., Meyer, V., Neuberger, A., Nielsen, K., Oliviero, M., Pampena, R., Paoli, J., Pawlik, E., Rao, B., Rendon, A., Russo, T., Sadek, A., Samhaber, K., Schneiderbauer, R., Schweizer, A., Toberer, F., Trennheuser, L., Vlahova, L., Wald, A., Winkler, J., Wölbing, P., Zalaudek, I. (2018). 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. https://doi.org/10.1093/annonc/mdy166

[35] Hasan, M., Barman, S. D., Islam, S., & Reza, A. W. (2019). Skin Cancer Detection Using Convolutional Neural Network. Proceedings of the 5th International Conference on Computing and Artificial Intelligence, 254–258. https://doi.org/10.1145/3330482.3330525.

[36] Hartanto, C. A., & Wibowo, A. (2020). Development of Mobile Skin Cancer Detection using Faster R-CNN and MobileNet v2 Model. Proceedings of the 7th International Conference on Information Technology, Computer, and Electrical Engineering (ICITACEE), 58–63. https://doi.org/10.1109/icitacee50144.2020.9239197

[37] He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep Residual Learning for Image Recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 770–778. https://doi.org/10.1109/cvpr.2016.90

[38] Helmold, M., & Terry, B. (2021). Industry 4.0 and artificial intelligence (AI). In Future of business and finance (pp. 77–83). https://doi.org/10.1007/978-3-030-68696-3_5

[39] Hermosilla, P., Soto, R., Vega, E., Suazo, C., & Ponce, J. (2024). Skin Cancer Detection and Classification Using Neural Network Algorithms: A Systematic review. Diagnostics, 14(4), 454. https://doi.org/10.3390/diagnostics14040454

[40] Himel, G. M. S., Islam, M. M., Al-Aff, K. A., Karim, S. I., & Sikder, M. K. U. (2024). Skin cancer segmentation and classification using Vision Transformer for automatic analysis in Dermatoscopy-Based noninvasive digital system. International Journal of Biomedical Imaging, 2024, 1–18. https://doi.org/10.1155/2024/3022192

[41] Hochreiter, S., & Schmidhuber, J. (1997). Long Short-Term memory. Neural Computation, 9(8), 1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735

[42] Howard, A. G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., & Adam, H. (2017). MobileNets: efficient convolutional neural networks for mobile vision applications. arXiv (Cornell University). https://doi.org/10.48550/arxiv.1704.04861

[43] Ijaz, H., Sultan, H., Altaf, M., & Waris, A. (2023). Embedded Skin Lesion Segmentation using Lightweight Encoder-Decoder Architectures. Proceedings of the 3rd International Conference on Artificial Intelligence (ICAI), 97, 176–181. https://doi.org/10.1109/icai58407.2023.10136688

[44] Ioffe, S., & Szegedy, C. (2015). Batch normalization: Accelerating deep network training by reducing internal covariate shift. arXiv.org. https://arxiv.org/abs/1502.03167

[45] Imran, A., Nasir, A., Bilal, M., Sun, G., Alzahrani, A., & Almuhaimeed, A. (2022). Skin cancer detection using combined decision of deep learners. IEEE Access, 10, 118198–118212. https://doi.org/10.1109/access.2022.3220329

[46] Indraswari, R., Rokhana, R., & Herulambang, W. (2022). Melanoma image classification based on MobileNetV2 network. Procedia Computer Science, 197(C), 198–207. https://doi.org/10.1016/j.procs.2021.12.132

[47] ISIC 2024 - Skin Cancer Detection with 3D-TBP. (n.d.). Kaggle. https://kaggle.com/competitions/isic-2024-challenge

[48] Jayaseeli, J. D. D., Briskilal, J., Fancy, C., Vaitheeshwaran, V., Patibandla, R. S. M. L., Syed, K., & Swain, A. K. (2025b). An intelligent framework for skin cancer detection and classification using fusion of Squeeze-Excitation-DenseNet with Metaheuristic-driven ensemble deep learning models. Scientific Reports, 15(1), 7425. https://doi.org/10.1038/s41598-025-92293-1

[49] Jojoa Acosta, M. F., Tovar, L. Y. C., Garcia-Zapirain, M. B., & Percybrooks, W. S. (2021). Melanoma diagnosis using deep learning techniques on dermatoscopic images. BMC Medical Imaging, 21(1), 6. https://doi.org/10.1186/s12880-020-00534-8

[50] Jozwik, M., Bednarczuk, K., & Osierda, Z. (2024). Dermatofibrosarcoma protuberans: An updated review of the literature. Cancers, 16(18), 3124. https://doi.org/10.3390/cancers16183124

[51] Kalouche, S. (2016) Vision-Based Classification of Skin Cancer Using Deep Learning. https://www.semanticscholar.org/paper/Vision-Based-Classification-of-Skin-Cancer-usingKalouche/b57ba909756462d812dc20fca157b3972bc1f533

[52] Karimi, A., Faez, K., & Nazari, S. (2023). DEU-Net: Dual-Encoder U-Net for automated skin lesion segmentation. IEEE Access, 11, 134804–134821. https://doi.org/10.1109/access.2023.3337528

[53] Kaushik, P., Rathore, R., Kumar, A., Kanishka, Goshi, G., & Sharma, P. (2024). Identifying Melanoma Skin Disease Using Convolutional Neural Network DenseNet-121. IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI), 2, 1-4. https://doi.org/10.1109/IATMSI60426.2024.10502880

[54] Khan, M. Q., Hussain, A., Rehman, S. U., Khan, U., Maqsood, M., Mehmood, K., & Khan, M. A. (2019). Classification of melanoma and nevus in digital images for diagnosis of skin cancer. IEEE Access, 7, 90132–90144. https://doi.org/10.1109/access.2019.2926837

[55] Khan, S., & Khan, A. (2023). SkinViT: A transformer based method for Melanoma and Nonmelanoma classification. PLoS ONE, 18(12), e0295151. https://doi.org/10.1371/journal.pone.0295151

[56] Khattar, S., & Kaur, R. (2022). Computer assisted diagnosis of skin cancer: A survey and future recommendations. Computers & Electrical Engineering, 104, 108431. https://doi.org/10.1016/j.compeleceng.2022.108431

[57] Kou, Y., Li, L., Li, H., Tan, Y., Li, B., Wang, K., & Du, B. (2016). Berberine suppressed epithelial mesenchymal transition through cross-talk regulation of PI3K/AKT and RARα/RARβ in melanoma cells. Biochemical and Biophysical Research Communications, 479(2), 290–296. https://doi.org/10.1016/j.bbrc.2016.09.061

[58] Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017b). ImageNet classification with deep convolutional neural networks. Communications of the ACM, 60(6), 84–90. https://doi.org/10.1145/3065386

[59] Kumar, A. & Sodhi, S. (2020). Comparative Analysis of Gaussian Filter, Median Filter and Denoise Autoenocoder. 45-51. https://doi.org/10.23919/INDIACom 49435.2020.9083712

[60] Kumar, M., Kumar, J., & Kukana, P. (2025). A Comprehensive Approach for Skin Cancer Detection and Classification Using Machine Learning and Deep Neural Network Algorithm. Proceedings of the IEEE International Conference on Cognitive Computing in Engineering, Communications, Sciences and Biomedical Health Informatics (IC3ECSBHI), 984–989. https://doi.org/10.1109/ic3ecsbhi63591.2025.10991337

[61] Lee, J. W., Hur, J., Yeo, K. Y., Yu, H. J., & Kim, J. S. (2009). A case of pigmented Bowen’s disease. Annals of Dermatology, 21(2), 197. https://doi.org/10.5021/ad.2009.21.2.197

[62] Leiter, U., Keim, U., & Garbe, C. (2020). Epidemiology of Skin Cancer: Update 2019. Advances in Experimental Medicine and Biology, 1268, 123–139. https://doi.org/10.1007/978-3-030-46227-7_6

[63] Linardatos, P., Papastefanopoulos, V., & Kotsiantis, S. (2020). Explainable AI: A review of Machine Learning Interpretability Methods. Entropy, 23(1), 18. https://doi.org/10.3390/e23010018

[64] Lio, P. A., & Nghiem, P. (2004). Interactive Atlas of Dermoscopy. Journal of the American Academy of Dermatology, 50(5), 807–808. https://doi.org/10.1016/j.jaad.2003.07.029

[65] Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. a. A., Ciompi, F., Ghafoorian, M., Van Der Laak, J. A., Van Ginneken, B., & Sánchez, C. I. (2017). A survey on deep learning in medical image analysis. Medical Image Analysis, 42, 60–88. https://doi.org/10.1016/j.media.2017.07.005

[66] Loescher, L. J., Janda, M., Soyer, H. P., Shea, K., & Curiel-Lewandrowski, C. (2013). Advances in skin cancer early detection and diagnosis. Seminars in Oncology Nursing, 29(3), 170–181. https://doi.org/10.1016/j.soncn.2013.06.003

[67] Lop Lopez-Martin, J. A., Fernández, A. A., Ríos-Martín, J. J., Hernández-Losa, J., Hernández, L. A., Fuentes, P. C., Reina, S. O., Izquierdo, E. O., Martí, R. M., García, J. S., Fábrega, B. F., & Peralto, J. L. R. (2020). Frequency and Clinicopathological Profile Associated with Braf Mutations in Patients with Advanced Melanoma in Spain. Translational Oncology, 13(6), 100750. https://doi.org/10.1016/j.tranon.2020.100750

[68] Loshchilov, I., & Hutter, F. (2016, August 13). SGDR: Stochastic Gradient Descent with Warm Restarts. arXiv.org. https://arxiv.org/abs/1608.03983

[69] Majtner, T., Yildirim-Yayilgan, S., & Hardeberg, J. Y. (2018). Optimised deep learning features for improved melanoma detection. Multimedia Tools and Applications, 78(9), 11883–11903. https://doi.org/10.1007/s11042-018-6734-6

[70] Mavaddati, S. (2024). Skin cancer classification based on a hybrid deep model and long short-term memory. Biomedical Signal Processing and Control, 100, 107109. https://doi.org/10.1016/j.bspc.2024.107109

[71] Mehra, A., Bhati, A., Kumar, A., & Malhotra, R. (2021). Skin cancer classification through transfer learning using REsNEt-50. In Advances in intelligent systems and computing, 55–62. https://doi.org/10.1007/978-981-33-4367-2_6

[72] Mendonca, T., Ferreira, P. M., Marques, J. S., Marcal, A. R. S., & Rozeira, J. (2013). PH2 - A dermoscopic image database for research and benchmarking. Proceedings of the 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2013, 5437–5440. https://doi.org/10.1109/embc.2013.6610779

[73] Mohan, J., Sivasubramanian, A., V, S., & Ravi, V. (2025). Enhancing skin disease classification leveraging transformer-based deep learning architectures and explainable AI. Computers in Biology and Medicine, 190, 110007. https://doi.org/10.1016/j.compbiomed.2025.110007

[74] Moturi, D., Surapaneni, R. K., & Avanigadda, V. S. G. (2024). Developing an efficient method for melanoma detection using CNN techniques. Journal of the Egyptian National Cancer Institute, 36(1), 6. https://doi.org/10.1186/s43046-024-00210-w

[75] Nasiri, S., Helsper, J., Jung, M., & Fathi, M. (2020). DePicT Melanoma Deep-CLASS: a deep convolutional neural networks approach to classify skin lesion images. BMC Bioinformatics, 21(S2), 84. https://doi.org/10.1186/s12859-020-3351-y

[76] Nasr-Esfahani, E., Samavi, S., Karimi, N., Soroushmehr, S., Jafari, M., Ward, K., & Najarian, K. (2016). Melanoma detection by analysis of clinical images using convolutional neural network. Proceedings of the 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2016, 1373–1376. https://doi.org/10.1109/embc.2016.7590963

[77] Qin, Z., Liu, Z., Zhu, P., & Xue, Y. (2020). A GAN-based image synthesis method for skin lesion classification. Computer Methods and Programs in Biomedicine, 195, 105568. https://doi.org/10.1016/j.cmpb.2020.105568

[78] Rahman, M. A., Fahad, N. M., Raiaan, M. a. K., Jonkman, M., De Boer, F., & Azam, S. (2025). Advancing skin cancer detection integrating a novel unsupervised classification and enhanced imaging techniques. CAAI Transactions on Intelligence Technology, 10(2), 474–493. https://doi.org/10.1049/cit2.12410

[79] Reddy, N. V. R., Deshmukh, A. A., Rao, V. S., Godla, S. R., El-Ebiary, Y. A., Bravo, L. M. R., & Manikandan, R. (2023). Enhancing Skin Cancer Detection Through an AI-Powered Framework by Integrating African Vulture Optimization with GAN-based Bi-LSTM Architecture. International Journal of Advanced Computer Science and Applications, 14(9). https://doi.org/10.14569/ijacsa.2023.0140960

[80] Ribeiro, M., Singh, S., & Guestrin, C. (2016). “Why Should I Trust You?”: Explaining the Predictions of Any Classifier. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 97–101. https://doi.org/10.18653/v1/n16-3020

[81] Sahu, S. P., Muduli, D., Das, S., Gouda, R. K., Sahu, P., & Sharma, S. K. (2024). Enhancing Skin Cancer Diagnosis with Customized InceptionV3: A Deep Learning Approach. Proceedings of the 15th International Conference on Computing Communication and Networking Technologies (ICCCNT), 1–7. https://doi.org/10.1109/icccnt61001.2024.10725670

[82] Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., & Chen, L. (2018). MobileNetV2: Inverted Residuals and Linear Bottlenecks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 4510–4520. https://doi.org/10.1109/cvpr.2018.00474

[83] Sedigh, P., Sadeghian, R., & Masouleh, M. T. (2019). Generating Synthetic Medical Images by Using GAN to Improve CNN Performance in Skin Cancer Classification. Proceedings of the 7th International Conference on Robotics and Mechatronics (ICRoM), 497–502. https://doi.org/10.1109/icrom48714.2019.9071823

[84] Senan, E. M., & Jadhav, M. E. (2021). Analysis of dermoscopy images by using ABCD rule for early detection of skin cancer. Global Transitions Proceedings, 2(1), 1–7. https://doi.org/10.1016/j.gltp.2021.01.001

[85] Shahin, A. H., Amer, K., & Elattar, M. A. (2019). Deep Convolutional Encoder-Decoders with Aggregated Multi-Resolution Skip Connections for Skin Lesion Segmentation. Proceedings of the 16th IEEE International Symposium on Biomedical Imaging (ISBI), 451–454. https://doi.org/10.1109/isbi.2019.8759172

[86] Shetty, B., Fernandes, R., Rodrigues, A. P., Chengoden, R., Bhattacharya, S., & Lakshmanna, K. (2022). Skin lesion classification of dermoscopic images using machine learning and convolutional neural network. Scientific Reports, 12(1), 18134. https://doi.org/10.1038/s41598-022-22644-9

[87] Shorten, C., & Khoshgoftaar, T. M. (2019). A survey on Image Data Augmentation for Deep Learning. Journal of Big Data, 6(1). https://doi.org/10.1186/s40537-019-0197-0

[88] Siddique, N., Paheding, S., Elkin, C. P., & Devabhaktuni, V. (2021). U-Net and its Variants for Medical Image Segmentation: A Review of Theory and Applications. IEEE Access, 9, 82031–82057. https://doi.org/10.1109/access.2021.3086020

[89] Sinz, C., Tschandl, P., Rosendahl, C., Akay, B. N., Argenziano, G., Blum, A., Braun, R. P., Cabo, H., Gourhant, J., Kreusch, J., Lallas, A., Lapins, J., Marghoob, A. A., Menzies, S. W., Paoli, J., Rabinovitz, H. S., Rinner, C., Scope, A., Soyer, HP., Thomas, L., Zalaudek, I., & Kittler, H. Accuracy of dermatoscopy for the diagnosis of nonpigmented cancers of the skin. Journal of the American Academy of Dermatology, 77(6), 1100–1109. https://doi.org/10.1016/j.jaad.2017.07.022

[90] Smith, L. N. (2017). Cyclical Learning Rates for Training Neural Networks. Proceedings of the IEEE Winter Conference on Applications of Computer Vision (WACV), 464–472. https://doi.org/10.1109/wacv.2017.58

[91] Srivastava, H., Geoffrey, N., Krizhevsky, A., Sutskever, I., Rachmad, Y., & Salakhutdinov, R. (2014). Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research. 15. 1929-1958.

[92] Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015). Going deeper with convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 1–9. https://doi.org/10.1109/cvpr.2015.7298594

[93] Tajbakhsh, N., Shin, J. Y., Gurudu, S. R., Hurst, R. T., Kendall, C. B., Gotway, M. B., & Liang, J. (2016). Convolutional neural networks for medical image analysis: full training or fine tuning? IEEE Transactions on Medical Imaging, 35(5), 1299–1312. https://doi.org/10.1109/tmi.2016.2535302

[94] Tan, C., Sun, F., Kong, T., Zhang, W., Yang, C., & Liu, C. (2018). A survey on Deep transfer learning. In Lecture notes in computer science (pp. 270–279). https://doi.org/10.1007/978-3-030-01424-7_27

[95] Trotter, S. C., Louie-Gao, Q., Hession, M. T., & Cummins, D. (2014). Skin Cancer Education for Massage Therapists: A Novel Approach to the Early Detection of Suspicious Lesions. Journal of Cancer Education, 29(2), 266–269. https://doi.org/10.1007/s13187-013-0589-3

[96] Tschandl, P., Rinner, C., Apalla, Z., Argenziano, G., Codella, N., Halpern, A., Janda, M., Lallas, A., Longo, C., Malvehy, J., Paoli, J., Puig, S., Rosendahl, C., Soyer, H. P., Zalaudek, I., & Kittler, H. (2020). Human–computer collaboration for skin cancer recognition. Nature Medicine, 26(8), 1229–1234. https://doi.org/10.1038/s41591-020-0942-0

[97] 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), 180161. https://doi.org/10.1038/sdata.2018.161

[98] Vig, L. (2014). Comparative analysis of different classifiers for the Wisconsin Breast Cancer Dataset. OALib, 01(06), 1–7. https://doi.org/10.4236/oalib.1100660

[99] Wang, R., Chen, X., Wang, X., Wang, H., Qian, C., Yao, L., & Zhang, K. (2024). A novel approach for melanoma detection utilizing GAN synthesis and vision transformer. Computers in Biology and Medicine, 176, 108572. https://doi.org/10.1016/j.compbiomed.2024.108572

[100] Wibowo, A., Purnama, S. R., Wirawan, P. W., & Rasyidi, H. (2021). Lightweight encoder-decoder model for automatic skin lesion segmentation. Informatics in Medicine Unlocked, 25, 100640. https://doi.org/10.1016/j.imu.2021.100640

[101] Yang, G., Luo, S., & Greer, P. (2023). A novel Vision transformer model for skin cancer classification. Neural Processing Letters, 55(7), 9335–9351. https://doi.org/10.1007/s11063-023-11204-5

[102] Yin, W., Huang, J., Chen, J., & Ji, Y. (2022). A study on skin tumor classification based on dense convolutional networks with fused metadata. Frontiers in Oncology, 12, 989894. https://doi.org/10.3389/fonc.2022.989894

[103] Yu, L., Chen, H., Dou, Q., Qin, J., & Heng, P. (2016). Automated melanoma recognition in dermoscopy images via very deep residual networks. IEEE Transactions on Medical Imaging, 36(4), 994–1004. https://doi.org/10.1109/tmi.2016.2642839

[104] Zakariah, M., Al-Razgan, M., & Alfakih, T. (2024). Skin cancer detection with MobileNet-based transfer learning and MixNets for enhanced diagnosis. Neural Computing and Applications, 36(34), 21383–21413. https://doi.org/10.1007/s00521-024-10227-w

[105] Zeinaty, P. E., Lebbé, C., & Delyon, J. (2023). Endemic Kaposi’s sarcoma. Cancers, 15(3), 872. https://doi.org/10.3390/cancers15030872

[106] Zhang, X., Zhou, X., Lin, M., & Sun, J. (2018). ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 6848–6856. https://doi.org/10.1109/cvpr.2018.00716

[107] Zhang, Y., Ostrowski, S. M., & Fisher, D. E. (2024). Nevi and melanoma. Hematology/Oncology Clinics of North America, 38(5), 939–952. https://doi.org/10.1016/j.hoc.2024.05.005

Zautomatyzowana diagnostyka raka skóry z wykorzystaniem głębokiego uczenia: systematyczny przegląd najnowocześniejszych architektur, technik i oceny wydajności. (2026). Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska, 16(1), 10-20. https://doi.org/10.35784/iapgos.7427

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