Title:Breast Cancer Detection and Classification using Traditional Computer Vision Techniques: A Comprehensive Review
VOLUME: 16 ISSUE: 10
Author(s):Saliha Zahoor, Ikram Ullah Lali, Muhammad Attique Khan*, Kashif Javed and Waqar Mehmood
Affiliation:Department of Computer Science, University of Gujrat, Gujrat, Department of Information Technology, University of Education, Lahore, Department of Computer Science, HITEC University, Museum Road Taxila, Rawalpindi, Department of Robotics, SMME NUST, Islamabad, Department of Computer Science, COMSATS University Islamabad, Wah Cantt
Keywords:Cancer, segmentation, features, classification, challenges, Computer-Aided Diagnosis (CAD).
Abstract:Breast Cancer is a common dangerous disease for women. Around the world, many women
have 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 save a women's life. To
detect the breast masses, microcalcifications, and malignant cells,different techniques are used in
the Computer-Aided Diagnosis (CAD) systems phases like preprocessing, segmentation, feature extraction,
and classification. We have reported a detailed analysis of different techniques or methods
with their usage and performance measurement. From the reported results, it is concluded that for
breast cancer survival, it is essential to improve the methods or techniques to diagnose it at an initial
stage by improving the results of the Computer-Aided Diagnosis systems. Furthermore, segmentation
and classification phases are also 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.
