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Breast Cancer Detection and Classification using Traditional Computer Vision Techniques: A Comprehensive Review

Author(s):

Saliha Zahoor, Ikram Ullah Lali, Muhammad Attique Khan*, Kashif Javed and Waqar Mehmood   Pages 1 - 14 ( 14 )

Abstract:


Breast Cancer is a common dangerous disease for women. In the world, many women died due to Breast cancer. However, in the initial stage, the diagnosis of breast cancer can save women's life. To diagnose cancer in the breast tissues there are several techniques and methods. The image processing, machine learning and deep learning methods and techniques are presented in this paper to diagnose the breast cancer. This work will be helpful to adopt better choices and reliable methods to diagnose breast cancer in an initial stage to survive the women's life. To detect the breast masses, microcalcifications, malignant cells the different techniques are used in the Computer-Aided Diagnosis (CAD) systems phases like preprocessing, segmentation, feature extraction, and classification. We have been reported a detailed analysis of different techniques or methods with their usage and performance measurement. From the reported results, it is concluded that for the survival of women’s life it is essential to improve the methods or techniques to diagnose breast cancer at an initial stage by improving the results of the Computer-Aided Diagnosis systems. Furthermore, segmentation and classification phases are challenging for researchers for the diagnosis of breast cancer accurately. Therefore, more advanced tools and techniques are still essential for the accurate diagnosis and classification of breast cancer.

Keywords:

Keywords: Cancer, segmentation, features, classification, challenges.

Affiliation:

Department of Computer Science, University of Gujrat, Department of Computer Science, University of Gujrat, Computer Science & Engineering, HITEC University, Museum Road Taxila, Department of Robotics, SMME NUST, Islamabad, Department of Computer Science, COMSATS University Islamabad, Wah Cantt



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