Applying Digital Image Processing and Machine Learning Techniques for Screening of Skin Lesions

Zahra Pourmand, Mahmood Shirali, Ali Mohammad Hadianfard



Introduction: The presence of pigmented skin lesions is a significant global concern in the prevention of skin cancer. Detecting skin cancer at an early stage is essential for proper management and effective treatment. This study aimed to combine image processing and data mining to develop an intelligent model to screen skin cancer from skin lesions.

Material and Methods: The images were taken in a clinic by smartphone. Patients over 40 years of age participated in the study. During the segmentation phase, the lesions were separated from the original images through machine vision techniques. Various features such as symmetry, border irregularity, color variation, and diameter were extracted from the images, while some features were also obtained through face-to-face examination. Finally, a neural network was employed to classify whether the lesion was cancerous or non-cancerous. In addition, MATLAB version 2022 was considered to design the model.

Results: The study results indicated excellent segmentation. Using a neural network-based model, skin lesions were classified with a high level of accuracy, with 98.4% accuracy and 97% sensitivity. The results indicated the designed model significantly screened skin cancer with high accuracy.

Conclusion: This model can help patients to manage self-care and become aware of their skin lesions before consulting a physician.


Supervised Machine Learning; Image Processing; Skin Neoplasms; Early Detection of Cancer; Tele-Medicine;


Yousef H, Alhajj M, Sharma S. Anatomy, skin (integument), epidermis. StatPearlis Publishing; 2017.

Avgeraki K. Skin lesion analysis towards melanoma detection from dermoscopic images using convolutional neural networks. University of Piraeus; 2021.

Le DN, Le HX, Ngo LT, Ngo HT. Transfer learning with class-weighted and focal loss function for automatic skin cancer classification. arXiv preprint. 2020: 200905977.

Shalhout SZ, Kaufman HL, Emerick KS, Miller DM. Immunotherapy for non-melanoma skin cancer: Facts and hopes. Clin Cancer Res. 2022; 28(11): 2211-20. PMID: 35121622 DOI: 10.1158/1078-0432.CCR-21-2971

Liu-Smith F, Jia J, Zheng Y. UV-induced molecular signaling differences in melanoma and non-melanoma skin cancer. Adv Exp Med Biol. 2017; 996: 27-40. PMID: 29124688 DOI: 10.1007/978-3-319-56017-5_3

Gurajala R. Skin cancer detection using region based segmentation. International Journal of Innovative Science & Technology. 2019; 6(4): 42-46.

Combalia M, Codella N, Rotemberg V, Carrera C, Dusza S, Gutman D, et al. Validation of artificial intelligence prediction models for skin cancer diagnosis using dermoscopy images: The 2019 international skin imaging collaboration grand challenge. Lancet Digit Health. 2022; 4(5): e330-9. PMID: 35461690 DOI: 10.1016/S2589-7500(22)00021-8

Kanimozhi T, Murthi A. Computer aided melanoma skin cancer detection using artificial neural network classifier. Singaporean Journal of Scientific Research. 2016; 8(2): 35-42.

Kasmi R, Mokrani K. Classification of malignant melanoma and benign skin lesions: implementation of automatic ABCD rule. IET Image Processing. 2016; 10(6): 448-55.

Monisha M, Suresh A, Bapu BT, Rashmi M. Classification of malignant melanoma and benign skin lesion by using back propagation neural network and ABCD rule. Cluster Computing. 2019; 22: 12897-907.

Diaz-Ramón JL, Gardeazabal J, Izu RM, Garrote E, Rasero J, Apraiz A, et al. Melanoma clinical decision support system: An artificial intelligence-based tool to diagnose and predict disease outcome in early-stage melanoma patients. Cancers (Basel). 2023; 15(7): 2174. PMID: 37046835 DOI: 10.3390/cancers15072174

Ozkan IA, Koklu M. Skin lesion classification using machine learning algorithms. International Journal of Intelligent Systems and Applications in Engineering. 2017; 5(4): 285-9.

Thanh DN, Erkan U, Prasath VS, Kumar V, Hien NN. A skin lesion segmentation method for dermoscopic images based on adaptive thresholding with normalization of color models. International Conference on Electrical and Electronics Engineering. IEEE; 2019.

Fan H, Xie F, Li Y, Jiang Z, Liu J. Automatic segmentation of dermoscopy images using saliency combined with Otsu threshold. Comput Biol Med. 2017; 85: 75-85. PMID: 28460258 DOI: 10.1016/j.compbiomed.2017.03.025

Oliveira RB. Pattern recognition in pigmented skin lesion images using ensemble methods [PhD Thesis]. Universidade do Porto (Portugal); 2017.

Rangraz Jeddi F, Arabfard M, Arabkermany Z, Gilasi H. The diagnostic value of skin disease diagnosis expert system. Acta Inform Med. 2016; 24(1): 30-3. PMID: 27046943 DOI: 10.5455/aim.2016.24.30-33

Setiawan AW. Image segmentation metrics in skin lesion: accuracy, sensitivity, specificity, dice coefficient, Jaccard index, and Matthews correlation coefficient. International Conference on Computer Engineering, Network, and Intelligent Multimedia. IEEE; 2020.

Castillejos-Fernández H, López-Ortega O, Castro-Espinoza F, Ponomaryov V. An intelligent system for the diagnosis of skin cancer on digital images taken with dermoscopy. Acta Polytechnica Hungarica. 2017; 14(3): 169-85.

Mikołajczyk A, Kwasigroch A, Grochowski M. Intelligent system supporting diagnosis of malignant melanoma. Trends in Advanced Intelligent Control, Optimization and Automation. Springer; 2017.

Tan TY, Zhang L, Jiang M. An intelligent decision support system for skin cancer detection from dermoscopic images. International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery. IEEE; 2016.

Goyal M, Knackstedt T, Yan S, Hassanpour S. Artificial intelligence-based image classification methods for diagnosis of skin cancer: Challenges and opportunities. Comput Biol Med. 2020; 127: 104065. PMID: 33246265 DOI: 10.1016/j.compbiomed.2020.104065

Cheng B, Erdos D, Stanley RJ, Stoecker WV, Calcara DA, Gómez DD. Automatic detection of basal cell carcinoma using telangiectasia analysis in dermoscopy skin lesion images. Skin Res Technol. 2011; 17(3): 278-87. PMID: 23815446 DOI: 10.1111/j.1600-0846.2010.00494.x

Oliveira RB, Marranghello N, Pereira AS, Tavares JMR. A computational approach for detecting pigmented skin lesions in macroscopic images. Expert Systems with Applications. 2016; 61: 53-63.

Jones OT, Ranmuthu CK, Hall PN, Funston G, Walter FM. Recognising skin cancer in primary care. Adv Ther. 2020; 37(1): 603-16. PMID: 31734824 DOI: 10.1007/s12325-019-01130-1



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