@article { author = {S R, Sannasi Chakravarthy and Rajaguru, Harikumar}, title = {Comparison Analysis of Linear Discriminant Analysis and Cuckoo-Search Algorithm in the Classification of Breast Cancer from Digital Mammograms}, journal = {Asian Pacific Journal of Cancer Prevention}, volume = {20}, number = {8}, pages = {2333-2337}, year = {2019}, publisher = {West Asia Organization for Cancer Prevention (WAOCP), APOCP's West Asia Chapter.}, issn = {1513-7368}, eissn = {2476-762X}, doi = {10.31557/APJCP.2019.20.8.2333}, abstract = {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.}, keywords = {breast cancer,Mammogram,discriminant Analysis,cuckoo-search}, url = {https://journal.waocp.org/article_88686.html}, eprint = {https://journal.waocp.org/article_88686_c203f6e772f4fe387c0ec631eae30440.pdf} }