An Analytical and Comparative Study of Modern Intelligent Models for Skin Cancer Diagnosis
DOI:
https://doi.org/10.29304/jqcsm.2026.18.12671Keywords:
Skin cancer, Transfer learning, intra-class variability, CNN, HAM10000Abstract
Early diagnosis of skin cancer especially melanoma is a significant issue because of the visual similarities of lesion types, intra class variability, and drawbacks of the manual diagnosis. Over the past years, there is a wide range of artificial intelligence (AI) methods suggested, but the results reported were quite different because of the variations in datasets, preprocessing pipelines, model architectures, and evaluation protocols. This is a systematic review article of 29 peer-reviewed studies published 2015–2025 that were identified in major scientific databases and divided into three key categories, namely traditional machine learning (ML), deep learning (DL), and transfer learning (TL) frameworks. Comparatively, the studies are assessed by aspects of characteristics of the dataset, feature representation strategies, model complexity, diagnostic performance and clinical applicability. The discussion has shown that deep and transfer learning models tend to perform better than traditional techniques of ML in terms of classification accuracy, yet various issues on the class imbalance, data heterogeneity, the ability to perform generalization as well as efficiency of computation still exist. Another critical research gap that has been found in the literature is the lack of a standardized analytical framework to compare the ML, DL, and TL models under standardized assessment procedures taking into account the real-life limitations in clinical deployment. This paper is a comparative perspective on the current intelligent skin cancer diagnostic systems in a structured way and it identifies the future research directions of stronger, scalable and clinically sound AI-based solutions.
Downloads
References
R. Hamsalekha, G. D. George, and T. Y. Satheesha, “A Novel Deep Learning Approach for Automated Melanoma Classification using Hybrid CNN and Vision Transformer Model,” Fusion Pract. Appl., vol. 19, no. 2, pp. 92–101, 2025, doi: 10.54216/FPA.190207.
K. Mridha, M. M. Uddin, J. Shin, S. Khadka, and M. F. Mridha, “An interpretable skin cancer classification using optimized convolutional neural network for a smart healthcare system,” IEEE Access, vol. 11, pp. 41003–41018, 2023.
K. Nawaz et al., “Skin cancer detection using dermoscopic images with convolutional neural network,” Sci. Rep., vol. 15, no. 1, p. 7252, 2025.
V. Dillshad, M. A. Khan, M. Nazir, O. Saidani, N. Alturki, and S. Kadry, “D2LFS2Net: Multi‐class skin lesion diagnosis using deep learning and variance‐controlled Marine Predator optimisation: An application for precision medicine,” CAAI Trans. Intell. Technol., vol. 10, no. 1, pp. 207–222, 2025.
K. Razzaq and M. Shah, “Machine learning and deep learning paradigms: From techniques to practical applications and research frontiers,” Computers, vol. 14, no. 3, p. 93, 2025.
Z. C. Oleiwi, E. N. AlShemmary, and S. Al-Augby, “Multi-label Classification Technique of Chest X-Rays Image Based Cardiomegaly Disease Prediction,” in 2023 International Conference on Information Technology, Applied Mathematics and Statistics (ICITAMS), IEEE, 2023, pp. 134–139.
U. K. Lilhore et al., “SkinEHDLF a hybrid deep learning approach for accurate skin cancer classification in complex systems,” Sci. Rep., vol. 15, no. 1, p. 14913, 2025.
P. Tschandl, “The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions,” Harvard Dataverse. doi: doi:10.7910/DVN/DBW86T.
M. Shaheer, T. Arooj, H. Adeel, T. Saleem, and I. R. Rao, “AI-based skin cancer detection algorithms: opportunities, challenges and a way forward,” Asian J. Sci. Eng. Technol., vol. 4, no. 1, pp. 87–109, 2025.
V. Rotemberg et al., “A patient-centric dataset of images and metadata for identifying melanomas using clinical context,” Sci. data, vol. 8, no. 1, p. 34, 2021.
J. Amin, M. Azhar, H. Arshad, A. Zafar, and S.-H. Kim, “Skin-lesion segmentation using boundary-aware segmentation network and classification based on a mixture of convolutional and transformer neural networks,” Front. Med., vol. 12, p. 1524146, 2025.
S. A. Amiri, M. Nasrolahzadeh, Z. Mohammadpoory, and A. H. Z. Kordkheili, “Skin Lesion Classification via ensemble method on deep learning,” Multimed. Tools Appl., vol. 84, no. 18, pp. 19379–19397, 2025.
H. Eghtesaddoust, M. Valizadeh, and M. C. Amirani, “Fusion of Deep and Time–Frequency Local Features for Melanoma Skin Cancer Detection,” Appl. Comput. Intell. Soft Comput., vol. 2025, no. 1, p. 4767052, 2025.
A. T. P. Nguyen, R. M. Jewel, and A. Akter, “Comparative analysis of machine learning models for automated skin cancer detection: Advancements in diagnostic accuracy and ai integration,” Am. J. Med. Sci. Pharm. Res., vol. 7, no. 01, pp. 15–26, 2025.
H. Vega-Huerta et al., “Classification Model of Skin Cancer Using Convolutional Neural Network,” Ing. des Syst. d’Information, vol. 30, no. 2, p. 387, 2025.
A. Raza, A. Ali, S. Ullah, Y. N. Anjum, and B. Rehman, “Optimizing skin cancer screening with convolutional neural networks in smart healthcare systems,” PLoS One, vol. 20, no. 3, p. e0317181, 2025.
M. Gholizade, H. Soltanizadeh, M. Rahmanimanesh, and S. S. Sana, “A review of recent advances and strategies in transfer learning,” Int. J. Syst. Assur. Eng. Manag., pp. 1–40, 2025.
S. Mavaddati, “Skin cancer classification based on a hybrid deep model and long short-term memory,” Biomed. Signal Process. Control, vol. 100, p. 107109, 2025.
A. G. Diab, E.-S. M. El-Kenawy, N. F. F. Areed, H. M. Amer, and M. El-Seddek, “A metaheuristic optimization-based approach for accurate prediction and classification of knee osteoarthritis,” Sci. Rep., vol. 15, no. 1, p. 16815, 2025.
M. Juneja, N. Aggarwal, S. K. Saini, S. Pathak, M. Kaur, and M. Jaiswal, “A comprehensive review on artificial intelligence-driven preprocessing, segmentation, and classification techniques for precision furcation analysis in radiographic images,” Multimed. Tools Appl., vol. 84, no. 19, pp. 21467–21520, 2025.
B. Soundarya and C. Poongodi, “A novel hybrid feature fusion approach using handcrafted features with transfer learning model for enhanced skin cancer classification,” Comput. Biol. Med., vol. 190, p. 110104, 2025.
S. B. Atim and M. Y. I. I. Ibrahim, “Rule-Based Expert System Model with Backward Chaining Algorithm for Symptom-Based Skin Disease Diagnosis,” J. Teknol. dan Open Source, vol. 8, no. 1, pp. 288–294, 2025.
K. Niwa, K. Yane, Y. Saito, K. Hashikawa, and T. Nozaki, “Development of a Support Vector Machine-based Automated Facial Le Fort I Osteotomy Robot,” 2025.
P. Natha, S. P. Tera, R. Chinthaginjala, S. O. Rab, C. V. Narasimhulu, and T. H. Kim, “Boosting skin cancer diagnosis accuracy with ensemble approach,” Sci. Rep., vol. 15, no. 1, p. 1290, 2025.
O. N. Oyelade, E. F. Aminu, H. Wang, and K. Rafferty, “An adaptation of hybrid binary optimization algorithms for medical image feature selection in neural network for classification of breast cancer,” Neurocomputing, vol. 617, p. 129018, 2025.
Z. I. Kalantan, L. S. Alharbi, M. H. Al-Zahrani, and S. M. S. Binhimd, “Robust Dimensionality Reduction: A Bootstrap-Based Evaluation of PCA with Applications in Nutritional and Environmental Sciences,” Contemp. Math., pp. 923–942, 2025.
P. NATHA and R. POTHURAJU, “Skin cancer detection using machine learning classification models,” 2023.
I. A. Ozkan and M. Koklu, “Skin lesion classification using machine learning algorithms,” Int. J. Intell. Syst. Appl. Eng., vol. 5, no. 4, pp. 285–289, 2017.
S. Razia, S. Balakrishnan, M. A. Hussain, and P. Chakrabarti, “An Optimized Classification Framework for Skin Lesion Detection Using Machine Learning,” Eng. Technol. Appl. Sci. Res., vol. 15, no. 6, pp. 29605–29609, 2025.
P. Natha and P. RajaRajeswari, “Advancing skin cancer prediction using ensemble models,” Computers, vol. 13, no. 7, p. 157, 2024.
U.-O. Dorj, K.-K. Lee, J.-Y. Choi, and M. Lee, “The skin cancer classification using deep convolutional neural network,” Multimed. Tools Appl., vol. 77, no. 8, pp. 9909–9924, 2018.
L. Yu, H. Chen, Q. Dou, J. Qin, and P.-A. Heng, “Automated melanoma recognition in dermoscopy images via very deep residual networks,” IEEE Trans. Med. Imaging, vol. 36, no. 4, pp. 994–1004, 2016.
B. Mohammed and Ö. İnik, “Using Deep Learning Architectures For Skin Cancer Classification,” Celal Bayar Üniversitesi Fen Bilim. Derg., vol. 20, no. 4, pp. 82–91, 2024.
M. Badawi et al., “Skin cancer classification and segmentation using deep learning,” Int. J. Telecommun., vol. 4, no. 01, pp. 1–23, 2024.
B. Ozdemir and I. Pacal, “A robust deep learning framework for multiclass skin cancer classification,” Sci. Rep., vol. 15, no. 1, p. 4938, 2025.
A. A. Hussein, A. M. Montaser, and H. A. Elsayed, “Skin cancer image classification using hybrid quantum deep learning model with BiLSTM and MobileNetV2,” Quantum Mach. Intell., vol. 7, no. 2, p. 66, 2025.
J. Mohamed, N. S. Tezel, J. Rahebi, and R. Ghadami, “Melanoma Skin Cancer Recognition with a Convolutional Neural Network and Feature Dimensions Reduction with Aquila Optimizer,” Diagnostics, vol. 15, no. 6, p. 761, 2025.
G. Darian and N. Surantha, “Performance Evaluation of Convolutional Neural Network (CNN) for Skin Cancer Detection on Edge Computing Devices,” Appl. Sci., vol. 15, no. 6, p. 3077, 2025.
S. Karboua, F. Harrag, S. Karboua, A. Booutadjine, and M. Deriche, “A Deep Learning and Transfer Learning-Based Application for Skin Cancer Classification,” in 2025 IEEE 22nd International Multi-Conference on Systems, Signals & Devices (SSD), IEEE, 2025, pp. 745–750.
S. Remya, T. Anjali, and V. Sugumaran, “A novel transfer learning framework for multimodal skin lesion analysis,” IEEE Access, vol. 12, pp. 50738–50754, 2024.
P. Singh and J. Cirrone, “A data-efficient deep learning framework for segmentation and classification of histopathology images,” in European Conference on Computer Vision, Springer, 2022, pp. 385–405.
A. W. Salehi et al., “A study of CNN and transfer learning in medical imaging: Advantages, challenges, future scope,” Sustainability, vol. 15, no. 7, p. 5930, 2023.
A. K. Gairola, V. Kumar, and A. K. Sahoo, “Exploring the strengths of pre-trained CNN models with machine learning techniques for skin cancer diagnosis,” in 2022 IEEE 2nd Mysore Sub Section International Conference (MysuruCon), IEEE, 2022, pp. 1–6.
V. A. O. Nancy, P. Prabhavathy, M. S. Arya, and B. S. Ahamed, “Comparative study and analysis on skin cancer detection using machine learning and deep learning algorithms,” Multimed. Tools Appl., vol. 82, no. 29, pp. 45913–45957, 2023.
I. D. Mienye, T. G. Swart, G. Obaido, M. Jordan, and P. Ilono, “Deep convolutional neural networks in medical image analysis: A review,” Information, vol. 16, no. 3, p. 195, 2025.
F. Olayah, E. M. Senan, I. A. Ahmed, and B. Awaji, “AI techniques of dermoscopy image analysis for the early detection of skin lesions based on combined CNN features,” Diagnostics, vol. 13, no. 7, p. 1314, 2023.
N. Spolaor et al., “Fine-tuning pre-trained neural networks for medical image classification in small clinical datasets,” Multimed. Tools Appl., vol. 83, no. 9, pp. 27305–27329, 2024.
S. Al-Azad and S. Ahmmed, “Melanoma Detection with MobileNetV2 and Channel Attention: Addressing the Diverse Skin Tone Challenge,” in 2025 3rd International Conference on Intelligent Systems, Advanced Computing and Communication (ISACC), IEEE, 2025, pp. 1269–1274.
S. S. Zareen, G. Sun, M. Kundi, S. F. Qadri, and S. Qadri, “Enhancing Skin Cancer Diagnosis with Deep Learning: A Hybrid CNN-RNN Approach.,” Comput. Mater. Contin., vol. 79, no. 1, 2024.
E. H. I. Eliwa, “Enhancing Skin Cancer Diagnosis Through Fine‐Tuning of Pretrained Models: A Two‐Phase Transfer Learning Approach,” Int. J. Breast Cancer, vol. 2025, no. 1, p. 4362941, 2025.
S. Mustafa, A. Jaffar, M. Rashid, S. Akram, and S. M. Bhatti, “Deep learning-based skin lesion analysis using hybrid ResUNet++ and modified AlexNet-Random Forest for enhanced segmentation and classification,” PLoS One, vol. 20, no. 1, p. e0315120, 2025.
A. R. Khan, M. Mujahid, F. S. Alamri, T. Saba, and N. Ayesha, “Early‐Stage Melanoma Cancer Diagnosis Framework for Imbalanced Data From Dermoscopic Images,” Microsc. Res. Tech., vol. 88, no. 3, pp. 797–809, 2025.
A. K. W. Lee et al., “Artificial intelligence application in diagnosing, classifying, localizing, detecting and estimation the severity of skin condition in aesthetic medicine: a review,” Dermatological Rev., vol. 6, no. 1, p. e70015, 2025.
A. Alotaibi and D. AlSaeed, “Skin cancer detection using transfer learning and deep attention mechanisms,” Diagnostics, vol. 15, no. 1, p. 99, 2025.
A. Aboulmira et al., “Hybrid model with wavelet decomposition and EfficientNet for accurate skin cancer classification,” J. Cancer, vol. 16, no. 2, p. 506, 2025.
M. A. Khan et al., “Automatic melanoma and non-melanoma skin cancer diagnosis using advanced adaptive fine-tuned convolution neural networks,” Discov. Oncol., vol. 16, no. 1, p. 645, 2025.
F. Güler and M. Ağraz, “Investigation of Binary and Multiclass Classification Performance of Skin Cancer Images Using Transfer Learning Methods,” J. Clin. Pract. Res., vol. 47, no. 3, p. 235, 2025.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Zahraa Saad Abdul Wahid, Zahraa Ch. Oleiwi, Rasha Falah kadhem
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
