Mammogram classification using contourlet features with forest optimization-based feature selection approach

Breast cancer continues to be one of the major health issues across the world and it is mostly observed in females. However, the actual cause of this cancer is still an ongoing research topic. Hence, early detection and diagnosis of breast cancer are considered to be an effective and reliable solution. Mammography is one of the most efficacious medical tools for early detection of breast cancer. The radiologists identify the suspicious regions in the breast by carefully examining the mammograms. However, mammograms are sometimes difficult to analyze when the breast tissues are dense. Therefore, a computer-aided diagnosis (CAD) system is adopted which can improve the decisions of the radiologists. This paper proposes a hybrid CAD framework to classify the suspicious regions into either normal or abnormal, and further, benign or malignant. The proposed framework constitutes four computational modules, namely, ROI generation using cropping operation, texture feature extraction using contourlet transformation, a wrapper-based feature selection algorithm, namely, forest optimization algorithm to select the optimal features, and finally different classifiers like SVM, k-NN, Naïve Bayes, and C4.5 that are employed to classify the inputs into normal or abnormal, and again benign or malignant. The proposed framework is examined on two widely used standard datasets, namely, MIAS and DDSM. The performance measures are computed with respect to normal vs. abnormal, and benign vs. malignant for four different hybrid CAD models, namely, (Contourlet + FOA + SVM), (Contourlet + FOA + k-NN), (Contourlet + FOA + Naïve Bayes), and (Contourlet + FOA + C4.5). The highest classification accuracy of 100% is achieved for normal vs. abnormal classification in case of both MIAS and DDSM. The performance of the proposed hybrid scheme demonstrates its effectiveness with the other state-of-the-art schemes. Experimental results reveal that the proposed hybrid scheme is accurate and robust. Finally, the suggested scheme is considered as a reliable CAD framework to help the physicians for better diagnosis.

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