Medical imaging technique using curvelet transform and machine learning for the automated diagnosis of breast cancer from thermal image

Thermography is a useful imaging tool using infrared for the early diagnosis of breast cancer. Screening cancer aims to outstrip prognosis by seeing the precancerous stage to give a prominent prescription. Early diagnosis is essential to avoid the fatality rate in abnormal cases. In this article, a novel approach is proposed using image analysis and machine learning techniques. In the present work, thermal images were collected from the visual laboratory. In the pre-processing stage, the contrast of the image is improved by combining top-hat and bottom-hat transforms. The ROI extraction method is the preliminary process to select the right and left breast region and remove the neck and armpit region. Then, the imperfection in the structure of the image has been eliminated by using morphological operations. Statistical, geometrical, and intensity features are extracted from the pre-processed and segmented images. Texture features using a Gray-Level Co-Occurrence matrix are obtained both in the spatial domain and curvelet domain. The curvelet transform is used in the feature extraction stage, and this can be used to find an explanation of the curve discontinuity. The curvelet wrapping is applied, followed by the application of GLCM to extract texture features. In the proposed method, 16 features are used for the automated classification of input thermal images. Different machine learning techniques are explored, and the cubic SVM renders the highest accuracy of 93.3%. A combination of statistical, intensity, geometry features, and texture features extracted from curvelet coefficients provides the highest accuracy.