Comparison Feed Forward Back Propagation Networks (FFBPNs) with Support Vector Machine (SVM) for Diagnosis Skin Cancer Based on Images

Authors

  • Rawaa Jawad University of Technology, Department of Mechanical Engineering, Baghdad
  • Raheel Jawad Mechanical Engineering Department, University of Technology, Baghdad, Iraq

DOI:

https://doi.org/10.26740/vubeta.v2i2.36117

Keywords:

Diagnosis, ANN, SVM, FFBP, Skin Cancer

Abstract

Skin cancer is a type of malignancy responsible for 70 percent of overall skin cancer-related death worldwide. The purpose of the research is to use AI to detect skin cancer of all types more quickly and improve the efficiency of diagnostic radiology.The method used in this paper is an artificial neural network implemented for the detection of skin cancer and the watershed segmentation method for segmentation. The features extracted are shape and Gray-Level Co-Occurrence Matrix. The extracted feature is used for classification. The classifiers are Support Vector Machine and Feedforward Back Propagation applied in a Matlab environment and an image processing technique on a set of photographs that were collected from several websites, including the Kaggle web. The implementation of code for the detection of skin cancer by using data as 100 images 50 no cancer and 50 is cancer, the result shows a successful implementation for the detection of cancer in FFBP classifier a 45 and 2 is bad detection, as well as in SVM classifier 49 with 1 is bad diagnostic. The Conclusion shows SVM classifier provided results for the skin lesions classification produced 98% accuracy and the accuracy of the FFBP of 96 %. The conclusion of this study is helping people with skin cancer undergo a CT scan. The scan is tested using a computer trained to analyze CT scan data.

Author Biographies

Rawaa Jawad , University of Technology, Department of Mechanical Engineering, Baghdad

Rawaa Jawad is currently an academic staff at the department of mechanical engineering, University of Technology, Baghdad, Iraq. She is received her B.Sc. in Electrical Engineering from University of Technology, Baghdad, Iraq in 2010. In 2020 she has a master degree in Electronic and Engineering Technology, from Communication University of Baghdad, Iraq. She interested in Power Engineer, Image processing, Robotics, signal processing, Control System Artificial Intelligent, Optimization algorithms.

Raheel Jawad, Mechanical Engineering Department, University of Technology, Baghdad, Iraq

Raheel   Jawad is currently an academic staff at the department of electromechanical engineering, University of Technology, Baghdad, Iraq. she is received her B.Sc. in Electro Mechanical Engineering from University of Technology, Baghdad, Iraq in 2011. In 2018 she has a master    degree in Electro Mechanical Engineering (energy department) from University of Technology, Baghdad, Iraq. She interested in Power Engineering, Energy and Renewable energy, Control System, Artificial Intelligent and Optimization algorithms.

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2025-05-31

How to Cite

[1]
R. Jawad and R. Jawad, “Comparison Feed Forward Back Propagation Networks (FFBPNs) with Support Vector Machine (SVM) for Diagnosis Skin Cancer Based on Images ”, Vokasi Unesa Bull. Eng. Technol. Appl. Sci., vol. 2, no. 2, pp. 127–135, May 2025.

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