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Prathibha, B. N.
- Characterization of Breast Tissues in Combined Transforms Domain Using Support Vector Machines
Abstract Views :157 |
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Affiliations
1 Department of Computer Science and Engineering, Manonmaniam Sundaranar University, Tamil Nadu, IN
1 Department of Computer Science and Engineering, Manonmaniam Sundaranar University, Tamil Nadu, IN
Source
ICTACT Journal on Image and Video Processing, Vol 2, No 1 (2011), Pagination: 254-257Abstract
Mammography is a well established imaging technique for showing tissue abnormalities of breast and has been proven to reduce death rate due to breast cancer in screened populations of women. The proposed method classifies the breast tissues according to severity of abnormality (benign or malign) using combined transforms domain features. In this paper two such domains are explored, Discrete Cosine Transform - Discrete Wavelet Transform (DCT-DWT) and Discrete Cosine Transform - Stationary Wavelet Transform (DCT-SWT). The method is tested on 221 mammogram images from the MIAS database. The combined transform domain features proves to be a promising tool for precise classification with SVM classifier. The DCT-DWT domain yields 96.26% accuracy for discrimination between normal-malign samples comparing to DCT-SWT which gives 93.14%. The novelty of the proposed method is demonstrated by comparing with nearest neighbor classification technique.Keywords
Combined Transforms, Mammograms, SVM, Nearest Neighbor Classifier.
- Mammograms Analysis Using SVM Classifier in Combined Transforms Domain
Abstract Views :161 |
PDF Views:0
Authors
Affiliations
1 Department of Computer Science and Engineering, Manomaniam Sundaranar University, Tamil Nadu, IN
1 Department of Computer Science and Engineering, Manomaniam Sundaranar University, Tamil Nadu, IN
Source
ICTACT Journal on Image and Video Processing, Vol 1, No 3 (2011), Pagination: 172-177Abstract
Breast cancer is a primary cause of mortality and morbidity in women. Reports reveal that earlier the detection of abnormalities, better the improvement in survival. Digital mammograms are one of the most effective means for detecting possible breast anomalies at early stages. Digital mammograms supported with Computer Aided Diagnostic (CAD) systems help the radiologists in taking reliable decisions. The proposed CAD system extracts wavelet features and spectral features for the better classification of mammograms. The Support Vector Machines classifier is used to analyze 206 mammogram images from Mias database pertaining to the severity of abnormality, i.e., benign and malign. The proposed system gives 93.14% accuracy for discrimination between normal-malign and 87.25% accuracy for normal-benign samples and 89.22% accuracy for benign-malign samples. The study reveals that features extracted in hybrid transform domain with SVM classifier proves to be a promising tool for analysis of mammograms.Keywords
Mammograms, Classification, Hybrid Transforms, SVM.- Secure Association Rule Mining on Vertically Partitioned Data using Fully Homomorphic Encryption
Abstract Views :183 |
PDF Views:1
Authors
Affiliations
1 Department of Computer Science and Engineering, Manonmaniam Sundaranar University, IN
2 Department of Computer Science, G. Venkataswamy Naidu College, IN
1 Department of Computer Science and Engineering, Manonmaniam Sundaranar University, IN
2 Department of Computer Science, G. Venkataswamy Naidu College, IN
Source
ICTACT Journal on Soft Computing, Vol 11, No 4 (2021), Pagination: 2424-2428Abstract
Cloud Computing is a leading innovation technology that guides to access applications over the web. The data owner’s data can be gotten to and controlled in the cloud. Privacy has become conclusive in information-driven applications within the distributed outsourced information. There are numerous inquiries still coming up on the best way to accomplish a confided in a climate that monitors application and information in the cloud from unapproved users. For offering protection to the users, sporadically there is a need to encode the data before accomplishing any other process. The cryptography method is embraced for data privacy. In this paper, a privacy-preserving construction is intended for vertically partitioned data in the cloud with the assistance of the Fully Homomorphic Encryption method. In this work, the homomorphic encryption and the Fully Homomorphic Encryption method is taken into consideration. The performance of the Rule Mining algorithm namely Eclat is compared with the encryption algorithms. The examination result shows that the Fully Homomorphic Encryption is less time-consuming to generate rule in the cloud, regardless of the number of transactions.Keywords
Frequent Itemset Mining, Association Rule Mining, Homomorphic Encryption, Fully Homomorphic Encryption.References
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