TY - JOUR ID - 88686 TI - Comparison Analysis of Linear Discriminant Analysis and Cuckoo-Search Algorithm in the Classification of Breast Cancer from Digital Mammograms JO - Asian Pacific Journal of Cancer Prevention JA - APJCP LA - en SN - 1513-7368 AU - S R, Sannasi Chakravarthy AU - Rajaguru, Harikumar AD - Department of Electronics and Communication Engineering, Anna University (Bannari Amman Institute of Technology), Sathyamangalam, India. Y1 - 2019 PY - 2019 VL - 20 IS - 8 SP - 2333 EP - 2337 KW - breast cancer KW - Mammogram KW - discriminant Analysis KW - cuckoo-search DO - 10.31557/APJCP.2019.20.8.2333 N2 - Objective: Breast cancer is the most common invasive severity which leads to the second primary cause of deathamong women. The objective of this paper is to propose a computer-aided approach for the breast cancer classificationfrom the digital mammograms. Methods: Designing an effective classification approach will assist in resolving thedifficulties in analyzing digital mammograms. The proposed work utilized the Mammogram Image Analysis Society(MIAS) database for the analysis of breast cancer. Five distinct wavelet families are used for extraction of featuresfrom the mammograms of MIAS database. These extracted features are statistical in nature and served as input to theLinear Discriminant Analysis (LDA) and Cuckoo-Search Algorithm (CSA) classifiers. Results: Error rate, Sensitivity,Specificity and Accuracy are the performance measures used and the obtained results clearly state that the CSA usedas a classifier affords an accuracy of 97.5% while compared with the LDA classifier. Conclusion: The results ofcomparative performance analysis show that the CSA classifier outperforms the performance of LDA in terms of breastcancer classification. UR - https://journal.waocp.org/article_88686.html L1 - https://journal.waocp.org/article_88686_c203f6e772f4fe387c0ec631eae30440.pdf ER -