Hybrid Texture based Classification of Breast Mammograms using Adaboost Classifier

Breast cancer is one of the most dangerous, leading and widespread cancers in the world especially in women. For breast analysis, digital mammography is the most suitable tool used to take mammograms for detection of cancer. It has been proved in the literature that if it can be detected at early and initial stages, then there are many chances to cure timely and efficiently. Therefore, initial screening of mammograms is the most important to detect cancer at initial stages. A radiologist is very expensive in the whole world wide and for a common person, it is very difficult to take opinion from more than one radiologist because it is a very sensitive disease. Thus, another solution is required that can be used as a second opinion to help the low cost solution to the patients. In this paper, a solution has been proposed to solve such type of problem to take mammograms and then detect cancer automatically in those images without any help of radiologist or medical specialist. So this solution can be adopted especially at the initial level. Proposed method first segment the portion of the image that contains these cancerous parts. After that, enhancement has been performed so that cancer can be clearly visible and identifiable. Texture features have been extracted to classify mammograms. An ensemble classifier AdaBoost has been used to classify those features by using the concept of intelligent experts. The standard dataset has been used for validation of the proposed method by using well-known quantitative measures. Proposed method has been compared with the existing method. Results show that proposed method has achieved 96.74% accuracy as well as 98.34% sensitivity. Keywords—Features; Segmentation; Breast mammograms; Classification; Texture