Skin cancer classification has increasingly become an important application area for machine learning because conventional diagnostic workflows may require substantial clinical expertise, specialized imaging, and careful interpretation of visually similar skin lesions. This research presents a systematic analytical review of machine learning approaches used for automated skin cancer classification and diagnostic support, with particular emphasis on conventional machine learning, convolutional neural networks (CNNs), ensemble learning, transfer-oriented deep learning architectures, and hybrid classification systems. The study synthesizes the findings of 18 provided publications and comparatively examines their methodological characteristics, classification strategies, diagnostic objectives, and practical implications. The analysis indicates a progression from conventional classifiers such as K-nearest neighbors (KNN), support vector machines (SVM), random forests, and linear discriminant analysis toward CNN-based and ensemble deep-learning approaches. CNN architectures demonstrate strong potential for extracting discriminative visual features directly from skin lesion images, while hybrid and ensemble approaches seek to improve robustness by combining complementary learning mechanisms. Studies involving EfficientNets, parallel CNNs, combined deep learners, and CNN-SVM configurations further demonstrate the diversification of automated classification architectures. However, the literature also reveals persistent challenges involving model generalizability, dataset variability, interpretability, computational requirements, and clinical integration. The review consequently positions machine learning not as a replacement for dermatological expertise but as a decision-support technology capable of improving consistency, screening efficiency, and diagnostic prioritization when appropriately validated.
A Systematic Analysis of Machine Learning Approaches for Automated Skin Cancer Classification and Diagnostic Support
Abstract
References
A. E. Adeniyi et al., “Comparative Study for Predicting Melanoma Skin Cancer Using Linear Discriminant Analysis (LDA) and Classification Algorithms,” in The International Conference on Artificial Intelligence and Smart Environment, Springer, 2023, pp. 326–338.
M. Alzamel et al., “Advanced Skin Cancer Detection Using Deep Learning,” in International Conference on Engineering Applications of Neural Networks, Springer, 2023, pp. 267–278.
K. Ali, Z. A. Shaikh, A. A. Khan, and A. A. Laghari, “Multiclass skin cancer classification using EfficientNets–a first step towards preventing skin cancer,” Neurosci. Informatics, vol. 2, no. 4, p. 100034, 2022.
H. Bhatt, V. Shah, K. Shah, R. Shah, and M. Shah, “State-of-the-art machine learning techniques for melanoma skin cancer detection and classification: a comprehensive review,” Intell. Med., vol. 3, no. 3, pp. 180–190, 2023.
A. G. Diab, N. Fayez, and M. M. El-Seddek, “Accurate skin cancer diagnosis based on convolutional neural networks,” Indones. J. Electr. Eng. Comput. Sci., vol. 25, no. 3, pp. 1429–1441, 2022.
M. Q. Hatem, “Skin lesion classification system using a K-nearest neighbor algorithm,” Vis. Comput. Ind. Biomed. Art, vol. 5, no. 1, p. 7, 2022.
A. Imran, A. Nasir, M. Bilal, G. Sun, A. Alzahrani, and A. Almuhaimeed, “Skin cancer detection using combined decision of deep learners,” IEEE Access, vol. 10, pp. 118198–118212, 2022.
D. Keerthana, V. Venugopal, M. K. Nath, and M. Mishra, “Hybrid convolutional neural networks with SVM classifier for classification of skin cancer,” Biomed. Eng. Adv., vol. 5, p. 100069, 2023.
D. C. Malo, M. M. Rahman, J. Mahbub, and M. M. Khan, “Skin cancer detection using convolutional neural network,” in 2022 IEEE 12th Annual Computing and Communication Workshop and Conference (CCWC), IEEE, 2022, pp. 169–176.
S. Medhat, H. Abdel-Galil, A. E. Aboutabl, and H. Saleh, “Skin cancer diagnosis using convolutional neural networks for smartphone images: A comparative study,” J. Radiat. Res. Appl. Sci., vol. 15, no. 1, pp. 262–267, 2022.
A. Murugan, S. A. H. Nair, A. A. P. Preethi, and K. P. S. Kumar, “Diagnosis of skin cancer using machine learning techniques,” Microprocess. Microsyst., vol. 81, p. 103727, 2021.
O. K. Pal, “Skin Disease Classification: A Comparative Analysis of K-Nearest Neighbors (KNN) and Random Forest Algorithm,” in 2021 International Conference on Electronics, Communications and Information Technology (ICECIT), IEEE, 2021, pp. 1–5.
N. Rezaoana, M. S. Hossain, and K. Andersson, “Detection and classification of skin cancer by using a parallel CNN model,” in 2020 IEEE International Women in Engineering (WIE) Conference on Electrical and Computer Engineering (WIECON-ECE), IEEE, 2020, pp. 380–386.
M. A. Sabri, Y. Filali, H. El Khoukhi, and A. Aarab, “Skin cancer diagnosis using an improved ensemble machine learning model,” in 2020 International Conference on Intelligent Systems and Computer Vision (ISCV), IEEE, 2020, pp. 1–5.
E. M. Senan and M. E. Jadhav, “Diagnosis of dermoscopy images for the detection of skin lesions using SVM and KNN,” in Proceedings of Third International Conference on Sustainable Computing: SUSCOM 2021, Springer, 2022, pp. 125–134.
A. Shah et al., “A comprehensive study on skin cancer detection using artificial neural network (ANN) and convolutional neural network (CNN),” Clin. eHealth, 2023.
J. V Tembhurne, N. Hebbar, H. Y. Patil, and T. Diwan, “Skin cancer detection using ensemble of machine learning and deep learning techniques,” Multimed. Tools Appl., vol. 82, no. 18, pp. 27501–27524, 2023.
M. Vidya and M. V Karki, “Skin cancer detection using machine learning techniques,” in 2020 IEEE international conference on electronics, computing and communication technologies (CONECCT), IEEE, 2020, pp. 1–5.