Detection of Breast Cancer using Digital Image Processing Techniques
E. Kodhai1, S. Jaseema Yasmin2, K. Subhasree3, V. Vikneshwari4
1E. Kodhai, Department of Computer Science and Engineering, Sri ManakulaVinayagar Engineering College, Puducherry (Tamil Nadu), India.
2S. Jaseema Yasmin, Department of Computer Science and Engineering, Sri ManakulaVinayagar Engineering College, Puducherry (Tamil Nadu), India.
3K. Subhasree, Department of Computer Science and Engineering, Sri ManakulaVinayagar Engineering College, Puducherry (Tamil Nadu), India.
4V. Vikneshwari, Department of Computer Science and Engineering, Sri ManakulaVinayagar Engineering College, Puducherry (Tamil Nadu), India.
Manuscript received on 25 June 2019 | Revised Manuscript received on 13 July 2019 | Manuscript Published on 26 July 2019 | PP: 5-9 | Volume-8 Issue-2S2 July 2019 | Retrieval Number: B10020782S219/2019©BEIESP | DOI: 10.35940/ijrte.B1002.0782S219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Breast cancer, a major public health contention and currently causes an increased rate of cancer death in women. The preeminent intent of this project in medical diagnostics is by using mammography, that is a unique imaging technique in medicine for examining the breasts. A higher quality mammographic images (for example electronic pictures) are stored, mammography method (i.e exam) is performed, which is a prior stage for detection and diagnosis of bosom’s malignant growth. In order to detect the tumor FCM algorithm for segmentation is used and the features are extracted by using multi-level wavelet transformation technique with PCA and then some features are added with GLCM features. Further, those segmented region features are extracted and the dataset is trained and tested completely. The images are classified by using SVM, KNN, Tree classifier, Neural Networks or Naive Bayes classifier. Finally, the images from Kaggle dataset are compared and categorized as normal, benign or malignant tumors.
Keywords: FCM; GLCM- Gray Level Co-Occurrence Matrix; Bosom; Classifier; Mammogram.
Scope of the Article: Digital System and Logic Design