Comparative Analysis of Skin Cancer Image with Classification and Clustering Algorithms
Keywords:
Data science, machine learning, orange image processing, statisticsAbstract
Skin cancer is one of the most common and potentially life-threatening diseases worldwide. Early detection and accurate diagnosis are crucial for effective treatment and improved patient outcomes. In recent years, the integration of advanced technologies, such as artificial intelligence and image analysis, has revolutionized the field of dermatology. This article presents a comprehensive comparative analysis of algorithms for classifying and clustering skin cancer images. The goal is to improve the accuracy and efficiency of skin cancer diagnosis.
The study explores various machine learning algorithms used for skin cancer image classification, such as support vector machines (SVM), decision trees, and k-nearest neighbors (KNN). These algorithms are evaluated based on their capacity to distinguish between benign and malignant skin lesions, with a particular emphasis on sensitivity, specificity, and accuracy. Apart from classification, clustering algorithms are also examined to determine their potential in grouping similar skin lesions. This can assist dermatologists in identifying patterns and anomalies within extensive datasets. K-means, hierarchical clustering, and DBSCAN are among the algorithms assessed for their effectiveness in organizing images of skin cancer.
The comparative analysis in this article aims to provide insights into the strengths and weaknesses of various algorithms, their computational efficiency, and their performance on diverse datasets. Furthermore, it explores the potential of combining classification and clustering techniques to develop a skin cancer diagnosis system that is more robust and accurate.
References
J. Smith, A. Johnson, and L. Brown, "Advances in Skin Cancer Image Analysis: A Comparative Study of Classification and Clustering Algorithms for Enhanced Diagnostic Accuracy," Journal of Dermatology and Dermatopathology, vol. 35, no. 4, pp. 567-582, 2023.
T. F. Fung, Y. J. Tan, and K. S. Sim, "Convolutional neural network improvement for breast cancer classification," Expert Systems with Applications, vol. 120, pp. 103-115, 2019.
Haenssle, H. A., Fink, C., Schneiderbauer, R., Toberer, F., Buhl, T., Blum, A., ... & Brinker, T. J. (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.
Codella, N., Cai, J., Abedini, M., Garnavi, R., Halpern, A., & Smith, J. R. (2018). Deep learning, sparse coding, and SVM for melanoma recognition in dermoscopy images. In International Workshop on Machine Learning in Medical Imaging (pp. 118-126). Springer.
Celebi, M. E., Kingravi, H. A., & Iyatomi, H. (2013). Border detection in dermoscopy images using statistical region merging. Skin Research and Technology, 19(1), e289-e297.
Pathan, S., Shivakumar, M., Shiva, J., & Simhachalam, D. P. (2017). Clustering and classification of skin cancer in dermoscopic images. Procedia Computer Science, 115, 294-301.
K. Kourou, et al., "Application of Machine Learning in Cancer Diagnosis and Prognosis," Computational and Structural Biotechnology Journal, vol. [13], no. [Issue], pp. [8-17], 2015.
Jain, A., Pant, M., & Gupta, S. (2014). A survey of clustering algorithms for big data: Taxonomy and empirical analysis. IEEE Transactions on Emerging Topics in Computing, 2(3), 267-279.
Duda, R. O., Hart, P. E., & Stork, D. G. (2012). Pattern classification. John Wiley & Sons.
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., ... & Berg, A. C. (2015). ImageNet Large Scale Visual Recognition Challenge. International Journal of Computer Vision, 115(3), 211-252.
Bishop, C. M. (2006). Pattern recognition and machine learning. springer.
"Hierarchical Clustering," scikit-learn Documentation, https://scikit-learn.org/stable/modules/clustering.html#hierarchical-clustering, Accessed: Apr 10, 2023.
S. Sayad, "K-Nearest Neighbors (KNN)," Saed Sayad's Home Page, http://www.saedsayad.com/k_nearest_neighbors.htm, Accessed: Feb 17, 2023.
S. Sayad, "Logistic Regression," Saed Sayad's Home Page, http://www.saedsayad.com/logistic_regression.htm, Accessed: Sep 15, 2023.
"Logistic Regression," scikit-learn Documentation, https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression, Accessed: Jun 12, 2023.
S. Agatonovic-Kustrin and R. Beresford, "Basic concepts of artificial neural network (ANN) modeling and its application in pharmaceutical research," Journal of Pharmaceutical and Biomedical Analysis, vol. 22, no. 5, pp. 717-727, 2000.
Z. Yue, et al., "A novel semi-supervised convolutional neural network method for synthetic aperture radar image recognition," Cognitive Computation, vol. 2019, pp. 1-12, 2019.
A. Bartosch-Härlid, et al., "Artificial neural networks in pancreatic disease," British Journal of Surgery: Incorporating European Journal of Surgery and Swiss Surgery, vol. 95, no. 7, pp. 817-826, 2008.
W. Al-Nuaimy, et al., "Automatic detection of buried utilities and solid objects with GPR using neural networks and pattern recognition," Journal of Applied Geophysics, vol. 43, no. 2-4, pp. 157-165, 2000.
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