Exploring Innovations in Skin Cancer Detection: A Comprehensive Survey using Machine Learning and Deep Learning Approaches
Keywords:
Skin Cancer Detection, Machine Learning, Deep Learning, Convolutional Neural Networks, Dermoscopy, Image Analysis, Feature Engineering, Model Evaluation, Clinical TranslationAbstract
Skin cancer is one of the most prevalent types of cancer in the world, and prompt detection is essential to the effectiveness of therapy. The visual assessment made by dermatologists is a major component of conventional diagnostic approaches, which introduces subjectivity and the possibility of inaccuracy. Machine learning (ML) and deep learning (DL) techniques have emerged as promising tools to enhance the accuracy and efficiency of skin cancer detection. This survey paper provides a comprehensive overview of the recent advancements in the application of ML and DL for skin cancer detection. We analyze the commonly employed ML algorithms, including support vector machines (SVMs), decision trees, and random forests, as well as their performance in skin cancer classification tasks. We then focus on the transformational impact of DL, in analyzing dermoscopic and photographic images for skin lesion identification and segmentation. We systematically review the state-of-the-art ML and DL models, evaluating their accuracy, sensitivity, specificity, and computational efficiency and discuss the critical factors influencing model performance, such as dataset quality, feature engineering strategies, and hyper parameter optimization. Additionally, we address the challenges and limitations of current ML and DL approaches, including issues of data scarcity, model interpretability, and clinical validation. The paper concludes by identifying promising future research directions, emphasizing the need for larger and more diverse datasets, the development of explainable AI models, and the integration of ML and DL systems into clinical workflows to facilitate early and accurate skin identification of skin cancer, ultimately leading to better patient outcomes.
Downloads
References
M. Naqvi, Syed Qasim Gilani, T. Syed, O. Marques, and H.-C. Kim, “Skin Cancer Detection Using Deep Learning—A Review,” vol. 13, no. 11, pp. 1911–1911, May 2023, doi: https://doi.org/10.3390/diagnostics13111911.
V. Singh, V. K. Asari, and R. Rajasekaran, “A Deep Neural Network for Early Detection and Prediction of Chronic Kidney Disease,” Diagnostics, vol. 12, no. 1, p. 116, Jan. 2022, doi: https://doi.org/10.3390/diagnostics12010116.
P. M. M. Pereira et al., “Skin lesion classification enhancement using border-line features – The melanoma vs nevus problem,” Biomedical Signal Processing and Control, vol. 57, p. 101765, Mar. 2020, doi: https://doi.org/10.1016/j.bspc.2019.101765.
A. Esteva et al., “Dermatologist-level classification of skin cancer with deep neural networks,” Nature, vol. 542, no. 7639, pp. 115–118, Jan. 2017, doi: https://doi.org/10.1038/nature21056.
M. Binder, H. Kittler, A. Seeber, A. Steiner, H. Pehamberger, and K. Wolff, “Epiluminescence microscopy-based classification of pigmented skin lesions using computerized image analysis and an artificial neural network,” Melanoma Research, vol. 8, no. 3, pp. 261–266, Jun. 1998, doi: https://doi.org/10.1097/00008390-199806000-00009.
H. Kittler, H. Pehamberger, K. Wolff, and M. Binder, “Diagnostic accuracy of dermoscopy,” The Lancet. Oncology, vol. 3, no. 3, pp. 159–65, 2002, doi: https://doi.org/10.1016/s1470-2045(02)00679-4.
X. Fan, H. Sun, Z. Yuan, Z. Li, R. Shi, and N. Ghadimi, “High Voltage Gain DC/DC Converter Using Coupled Inductor and VM Techniques,” IEEE Access, vol. 8, pp. 131975–131987, Jan. 2020, doi: https://doi.org/10.1109/access.2020.3002902.
N. B. Linsangan, J. J. Adtoon, and J. L. Torres, “Geometric Analysis of Skin Lesion for Skin Cancer Using Image Processing,” 2018 IEEE 10th International Conference on Humanoid, Nanotechnology, Information Technology,Communication and Control, Environment and Management (HNICEM), Nov. 2018, doi: https://doi.org/10.1109/hnicem.2018.8666296.
T. Saba, “Recent advancement in cancer detection using machine learning: Systematic survey of decades, comparisons and challenges,” Journal of Infection and Public Health, vol. 13, no. 9, pp. 1274–1289, Sep. 2020, doi: https://doi.org/10.1016/j.jiph.2020.06.033.
M. I. Sharif, J. P. Li, J. Naz, and I. Rashid, “A comprehensive review on multi-organs tumor detection based on machine learning,” Pattern Recognition Letters, vol. 131, pp. 30–37, Mar. 2020, doi: https://doi.org/10.1016/j.patrec.2019.12.006.
H. Alquran et al., “The melanoma skin cancer detection and classification using support vector machine,” IEEE Xplore, Oct. 01, 2017. https://ieeexplore.ieee.org/abstract/document/8257738 (accessed Mar. 08, 2020).
H. Nahata and S. P. Singh, “Deep learning solutions for skin cancer detection and diagnosis,” in Learning and analytics in intelligent systems, 2020, pp. 159–182. doi: 10.1007/978-3-030-40850-3_8.
K. M. Hosny, M. A. Kassem, and M. M. Foaud, “Skin Cancer Classification using Deep Learning and Transfer Learning,” IEEE Xplore, Dec. 01, 2018. https://ieeexplore.ieee.org/abstract/document/8641762 (accessed Jan. 12, 2022).
T. Saba, M. A. Khan, A. Rehman, and S. L. Marie-Sainte, “Region Extraction and Classification of Skin Cancer: A Heterogeneous framework of Deep CNN Features Fusion and Reduction,” Journal of Medical Systems, vol. 43, no. 9, Jul. 2019, doi: https://doi.org/10.1007/s10916-019-1413-3.
S. Chatterjee, D. Dey, S. Munshi, and S. Gorai, “Extraction of features from cross correlation in space and frequency domains for classification of skin lesions,” Biomedical Signal Processing and Control, vol. 53, p. 101581, Aug. 2019, doi: https://doi.org/10.1016/j.bspc.2019.101581.
J. Arevalo, A. Cruz-Roa, V. Arias, E. Romero, and F. A. González, “An unsupervised feature learning framework for basal cell carcinoma image analysis,” Artificial Intelligence in Medicine, vol. 64, no. 2, pp. 131–145, Jun. 2015, doi: https://doi.org/10.1016/j.artmed.2015.04.004.
D. Bi, D. Zhu, Fatima Rashid Sheykhahmad, and M. Qiao, “Computer-aided skin cancer diagnosis based on a New meta-heuristic algorithm combined with support vector method,” Biomedical Signal Processing and Control, vol. 68, pp. 102631–102631, Jul. 2021, doi: https://doi.org/10.1016/j.bspc.2021.102631.
A. Esteva et al., “Dermatologist-level classification of skin cancer with deep neural networks,” Nature, vol. 542, no. 7639, pp. 115–118, Jan. 2017, doi: https://doi.org/10.1038/nature21056.
S. Nematzadeh, F. Kiani, M. Torkamanian-Afshar, and N. Aydin, “Tuning hyperparameters of machine learning algorithms and deep neural networks using metaheuristics: A bioinformatics study on biomedical and biological cases,” Computational Biology and Chemistry, vol. 97, p. 107619, Apr. 2022, doi: https://doi.org/10.1016/j.compbiolchem.2021.107619.
S. M. Thomas, J. G. Lefevre, G. Baxter, and N. A. Hamilton, “Interpretable Deep Learning Systems for Multi-Class Segmentation and Classification of Non-Melanoma Skin Cancer,” Medical Image Analysis, p. 101915, Nov. 2020, doi: https://doi.org/10.1016/j.media.2020.101915.
Barata C, Celebi ME, Marques JS. A survey of feature extraction in der-moscopy image analysis of skin cancer. IEEE J Biomed Health Inform2018;23(3):1096–109.
Rehman A, Khan MA, Mehmood Z, Saba T, Sardaraz M, Rashid M. Microscopicmelanoma detection and classification: A framework of pixel-based fusionand multilevel features reduction. Microsc Res Tech 2020, http://dx.doi.org/10.1002/jemt.23429.
T. Saba, Saleh Al-Zahrani, A. Rehman, and Saud Islamic, “Expert System for Offline Clinical Guidelines and Treatment,” Jan. 2012.
V. Ramya, J. Navarajan, R. Prathipa, and L. Kumar, “Detection of melanoma skin cancer using digital camera images,” ARPN Journal of Engineering and Applied Sciences, vol. 10, pp. 3082–3085, 2015.
J. Premaladha and K. S. Ravichandran, “Novel Approaches for Diagnosing Melanoma Skin Lesions Through Supervised and Deep Learning Algorithms,” Journal of Medical Systems, vol. 40, no. 4, p. 96, Apr. 2016, doi: https://doi.org/10.1007/s10916-016-0460-2.
Bareiro Paniagua, L. R., Leguizamón Correa, D. N., Pinto-Roa, D. P., Vázquez Noguera, J. L., & Salgueiro Toledo, L. A. (2016). Computerized Medical Diagnosis of Melanocytic Lesions based on the ABCD approach. CLEI Electronic Journal, 19(2), 6-6.
S. A. Khan et al., “Lungs nodule detection framework from computed tomography images using support vector machine,” Microscopy Research and Technique, vol. 82, no. 8, pp. 1256–1266, Apr. 2019, doi: https://doi.org/10.1002/jemt.23275.
X. Dai, I. Spasic, B. Meyer, S. Chapman, and F. Andres, “Machine Learning on Mobile: An On-device Inference App for Skin Cancer Detection,” 2019 Fourth International Conference on Fog and Mobile Edge Computing (FMEC), Jun. 2019, doi: https://doi.org/10.1109/fmec.2019.8795362.
T. Majtner, S. Yildirim-Yayilgan, and J. Y. Hardeberg, “Optimised deep learning features for improved melanoma detection,” Multimedia Tools and Applications, vol. 78, no. 9, pp. 11883–11903, Oct. 2018, doi: https://doi.org/10.1007/s11042-018-6734-6.
V. Vipin, Malaya Kumar Nath, V. Sreejith, Nikhil Francis Giji, A. Ramesh, and Meera Nair M, “Detection of Melanoma using Deep Learning Techniques: A Review,” 2021 International Conference on Communication, Control and Information Sciences (ICCISc), Jun. 2021, doi: https://doi.org/10.1109/iccisc52257.2021.9484861.
E. Nasr-Esfahani et al., “Melanoma detection by analysis of clinical images using convolutional neural network,” IEEE Xplore, Aug. 01, 2016. https://ieeexplore.ieee.org/abstract/document/7590963 (accessed Feb. 03, 2022).
M. I. Attia, A. Khosravi, Saeid Nahavandi, and Anousha Yazdabadi, “Skin melanoma segmentation using recurrent and convolutional neural networks,” Jan. 2017, doi: https://doi.org/10.1109/isbi.2017.7950522.
S. Mukherjee, Akshat Malu, A.R. Balamurali, and P. Bhattacharyya, “TwiSent: A Multistage System for Analyzing Sentiment in Twitter,” arXiv (Cornell University), Sep. 2012.
Ravva Sai Sanketh, M. Bala, N. Reddy, and P. Kumar, “Melanoma Disease Detection Using Convolutional Neural Networks,” May 2020, doi: https://doi.org/10.1109/iciccs48265.2020.9121075.
Md. M. I. Rahi, F. T. Khan, M. T. Mahtab, A. K. M. Amanat Ullah, Md. G. R. Alam, and Md. A. Alam, “Detection Of Skin Cancer Using Deep Neural Networks,” 2019 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE), Dec. 2019, doi: https://doi.org/10.1109/csde48274.2019.9162400.
H. Nahata and S. P. Singh, “Deep learning solutions for skin cancer detection and diagnosis,” in Learning and analytics in intelligent systems, pp. 159–182, 2020.
doi: 10.1007/978-3-030-40850-3_8.
J. Daghrir, L. Tlig, M. Bouchouicha and M. Sayadi, " Melanoma skin cancer detection using deep learning and classical machine learning techniques: A hybrid approach " in 2020 5th international conference on advanced technologies for signal and image processing (ATSIP), pp. 1-5, 2020.
Y. Li and L. Shen, “Skin Lesion Analysis towards Melanoma Detection Using Deep Learning Network,” Sensors, vol. 18, no. 2, p. 556, Feb. 2018, doi: https://doi.org/10.3390/s18020556.
A. Aima and A. K. Sharma, “Predictive Approach for Melanoma Skin Cancer Detection using CNN,” SSRN Electronic Journal, 2019, doi: https://doi.org/10.2139/ssrn.3352407.
P. Banasode, M. Patil, and N. Ammanagi, “A Melanoma Skin Cancer Detection Using Machine Learning Technique: Support Vector Machine,” IOP Conference Series: Materials Science and Engineering, vol. 1065, no. 1, p. 012039, Feb. 2021, doi: https://doi.org/10.1088/1757-899x/1065/1/012039.
S. Mustafa, A. B. Dauda, and M. Dauda, “Image processing and SVM classification for melanoma detection,” 2017 International Conference on Computing Networking and Informatics (ICCNI), Oct. 2017, doi: https://doi.org/10.1109/iccni.2017.8123777.
D. De a Rodrigues, R. F. Ivo, S. C. Satapathy, S. Wang, D. J. Hemanth, and P. P. R. Filho, “A new approach for classification skin lesion based on transfer learning, deep learning, and IoT system,” Pattern Recognition Letters, vol. 136, pp. 8–15, Aug. 2020, doi: 10.1016/j.patrec.2020.05.019.
A. Victor and M. Ghalib, “Automatic Detection and Classification of Skin Cancer,” International Journal of Intelligent Engineering and Systems, vol. 10, no. 3, pp. 444–451, Jun. 2017, doi: https://doi.org/10.22266/ijies2017.0630.50.
A. Masood, A. Al- Jumaily, and K. Anam, “Self-supervised learning model for skin cancer diagnosis,” 2015 7th International IEEE/EMBS Conference on Neural Engineering (NER), Apr. 2015, doi: https://doi.org/10.1109/ner.2015.7146798.
A. A. Adegun and S. Viriri, “Deep Learning-Based System for Automatic Melanoma Detection,” IEEE Access, vol. 8, pp. 7160–7172, 2020, doi: https://doi.org/10.1109/access.2019.2962812.
K. Jayapriya and I. J. Jacob, “Hybrid fully convolutional networks‐based skin lesion segmentation and melanoma detection using deep feature,” International Journal of Imaging Systems and Technology, Nov. 2019, doi: https://doi.org/10.1002/ima.22377.
N. Nida, A. Irtaza, A. Javed, M. H. Yousaf, and M. T. Mahmood, “Melanoma lesion detection and segmentation using deep region based convolutional neural network and fuzzy C-means clustering,” International Journal of Medical Informatics, vol. 124, pp. 37–48, Apr. 2019, doi: https://doi.org/10.1016/j.ijmedinf.2019.01.005.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
