Skin Tumors Diagnosis Utilizing Case Based Reasoning and The Expert System

Abstract views: 1229 / PDF downloads: 825


  • Roza Fuad Majeed Information Technology Department, Technical College of Informatics, Sulaimani Polytechnic University, Sulaimani, Iraq
  • Soran AB. M. Saeed Vice presidence of Scientific Affairs, Sulaimani Polytechnic University, Sulaimani, Iraq
  • Dana Abdulmajeed Abdilkarim Medical laboratory, Technical College of Health, Sulaimani Polytechnic University, Sulaimani, Iraq
  • Haval Mohammed Sidqi Database Department, Computer Science Institute, Sulaimani Polytechnic University, Sulaimani, Iraq


Skin cancer is considered as the most type of cancer that happens in humans. Three basic types of cancer occur which are basal cell carcinoma (BCC), Squamous cell carcinoma (SCC). Skin cancer leads to death if it is not diagnosed in an early stage. Fortunately, early diagnosis of skin cancer raises the survival rate of victims. Computer-aided has a great role to detect skin cancer which leads to saving human life. Based on that, this study proposes a computer-aided diagnosis (CAD) system that detects skin cancer using digital images, techniques of image processing, by using the case-based reasoning and expert system. The main goal for designing this system is to create a cheap, easy-to-use, and relatively accurate system for detecting skin cancer in an early stage to save human life, raises the survival rate, and decreases the cost of the dermoscopy test.


Keywords: Skin cancer, BCC, SCC, melanoma, CBR, expert system.


[1] H. Yousef and S. Sharma, " Anatomy, Skin (Integument)," Epidermi., 2017.
[2] S. R. Silp and V. chidvila, "A Reveiw On Skin Cancer," international research journal of pharmacy, vol. 4, no. 8, pp. 1-8, 2013.
[3] M. I. Qadir, "Skin Cancer: Etiology And Management," Pakistan journal of pharmaceutical sciences, vol. 29, no. 3, pp. 1-6, 2016.
[4] Sh. Jaina, V. jagtap and N. Pise, "Computer Aided Melanoma Skin Cancer Detection Using Image," International Conference on Intelligent Computing, Communication & Convergence(ICCC),pp. 1-6, 2015.
[5] M. Chandrahasa , V. Vadigeri and D.Salecha, "Detection Of Skin Cancer Using Image Processing Techniques," International Journal of Modern Trends in Engineering and Research(IJMTER), vol. 3, no. 5, pp. 111-114, 2016.
[6] H. C.Fernández,O.L.Ortega and F. Castro-Espinozaa, V. Ponomaryov, "An Intelligent System For The Diagnosis Of Skin Cancer On Digital Images Taken With Dermoscopy," Acta Polytechnica Hungarica, vol. 14, no. 3, pp. 169-185, 2017.
[7] R. P. Periyasamy and V.Gayathiri, "Melanoma Detection Through K-Means Segmentation And Feature Extraction," International Research Journal of Engineering and Technology (IRJET), vol. 4, no. 5, pp. 1301-1305, 2017.
[8] M. Anas, R. K. Gupta and Dr. Sh. Ahmad, "Skin Cancer Classification Using K-Means Clustering," International Journal of Technical Research and Applications, vol. 5, no. 1, pp. 62-65, 2017.
[9] A.M. Khirala, A. Sibai, Y .A. Abdarrahim and M. A. Omer, "A Literature Study Of Wavelets And Their Applications," 10.13140/RG.2.2.22856.32008,2017.
[10] S. Kolkur, D.R. Kalbande and V. Kharkar, "Machine Learning Approaches to Multi-Class Human Skin Disease Detection," International Journal of Computational Intelligence Research, vol. 14, no. 1, pp. 1-12, 2018.
[11] M. A. M. Shukran, N. M. S. Ahmad, S. Ramli and F. Rahmat, "Melanoma Cancer Diagnosis Device Using Image Processing Techniques," International Journal of Recent Technology and Engineering (IJRTE), vol. 7, no. 5S7, pp. 490-494, 2019.
[12] S. Bindhu and M. Mohan , "Segmentation Of Skin Lesions Using Texture Distinctiveness Lesion Segmentation Algorithm," Algorithm, vol. 1, no. 2, pp. 56-60, 2015.
[13] S.Kannan, V. Gurusamy and G.Nalini,"Review On Image Segmentation Techniques," 2014.
[14] J.B. Lamy, B. Sekarb, G. Guezenneca, J. Bouauda and B.Séroussia, "Explainable artificial intelligence for breast cancer: A visual case-based reasoning approach," Artificial Intelligence In Medicine, 94. 10.1016/j.artmed.2019.01.001, 2019.
[15] J. Kawahara, A. Ben Taieb, and G. Hamarneh," Deep Features to Classify Skin Lesions," 1397-1400. 10.1109/ISBI.2016.7493528,2016.
[16] Vijayalakshmi M M," Melanoma Skin Cancer Detection using Image Processing and Machine Learning," International Journal of Trend in Scientific Research and Development (IJTSRD),vol. 3,no. 4,pp.780-784,2019.
[17] N. Codella, J. Cai, M. Abedini, R. Garnavi, A. Halpern and J. R. Smith," Deep Learning, Sparse Coding, and SVM for Melanoma Recognition in Dermoscopy Images," 10.1007/978-3-319-24888-2,2015.
[18] V. Pomponiu, H. Nejati and N. Cheung, "Deep mole: Deep neural networks for skin mole lesion classification," 2016 IEEE International Conference on Image Processing (ICIP), Phoenix, AZ, pp. 2623-26272016.
[19] S. Sachdeva, "Fitzpatrick skin typing: Applications in dermatology," Indian J Dermatol Venereol Leprol, vol. 75, no. 1, pp. 93-96, 2009.
[20] S. Chung, "Basal Cell Carcinoma," Continuing Medical Education, vol. 39, no. 2, pp. 166-170, 2012.
[21] P. P. de Freitas, C. G. Senna,M. Tabai,C. T. Chone and A. Altemani, "Metastatic Basal Cell Carcinoma: A Rare Manifestation of a Common Disease," Hindawi Case Reports in Medicine, vol. 2017, pp. 1-5, 2017.
[22] N. DurgaRao and G.Sudhavani, "A Survey on Skin Cancer Detection System," Journal of Engineering Research and Application, vol. 7, no. 6, pp. 59-64, 2017.
[23] W. Yan, I. I. Wistuba, M. R. Emmert-Buck and H. S. Erickson, "Review Article Squamous cell carcinoma - similarities and differences among anatomical sites," Am J Cancer Res, vol. 1, no. 3, pp. 1-26, 2011.
[24] P. Das, N.Deshmukh, N. Badore, C. Ghulaxe and P. Patel, "A Review Article on Melanoma," journal of pharmaceutical sciences and research, vol. 8, no. 2, pp. 112-117, 2016.
[25] S. Gupta and R. Singhal, "Fundamentals and Characteristics of an Expert System," International Journal on Recent and Innovation Trends in Computing and Communication, vol. 6, no. 3, pp. 110-114, 2013.
[26] S. V. Shokouhi , p .Skalle and A.Aamodt, "An overview of case-based reasoning applications in drilling engineering," Artificial Intelligence Review, 41. 10.1007/s10462-011-9310-2, 2014.
[27] M. A. Mohammed, B. Al-Khateeb and D. A.Ibrahim, "Case based Reasoning Shell Frameworkas Decision Support Tool," Indian Journal of Science and Technology, vol. 9, no. 42, pp. 1-8, 2016.
[28] H. Y. A. Abutair, A. Belghith, "Using Case-Based Reasoning for Phishing Detection ," Procedia Computer Science, 109. 281-288. 10.1016/j.procs.2017.05.352,2017.
[29] Melanoma images, DermIS
[30] S. Sachdeva," Fitzpatrick skin typing: Applications in dermatology," Indian journal of dermatology, venereology and leprology. 75. 93-6. 10.4103/0378-6323.45238,2009.
[31] A. Wasilewska , J. Pauk and M. I. Touski," Image Processing Techniques For Roi Identification In Rheumatoid Arthritis Patients From Thermal Images," Acta Mechanica et Automatica, vol. 12, no. 1,pp. 49-53,2018.
[32] S. Ross-Howe and H.R. Tizhoosh ," The Effects of Image Pre- and Post-Processing, Wavelet Decomposition, and Local Binary Patterns on U-Nets for Skin Lesion Segmentation" Accepted for publication in proceedings of the IEEE World Congress on Computational Intelligence (IEEE WCCI),Rio de Janeiro, Brazil, 8-3 July, 2018.
[33] C. Saravanan, "Color Image to Grayscale Image Conversion," 196 - 199. 10.1109/ICCEA.2010.192,2010.
[34] A. Kaur, L. Kaur and S. Gupta," Image Recognition using Coefficient of Correlation and Structural Similarity Index in Uncontrolled Environment," International Journal of Computer Applications, vol. 59, no. 5, pp. 32-39,2012.


How to Cite

R. F. Majeed, S. AB. M. Saeed, D. Abdulmajeed Abdilkarim, and H. Mohammed Sidqi, “Skin Tumors Diagnosis Utilizing Case Based Reasoning and The Expert System”, KJAR, vol. 5, no. 1, pp. 96–114, Jun. 2020, doi: 10.24017/science.2020.1.10.

Article Metrics





Pure and Applied Science