Detection of Breast Cancer on Magnetic Resonance Imaging Using Hybrid Feature Extraction and Deep Neural Network Techniques

Abstract

Breast cancer is one of the most occurring cancers in women due to the uncontrolled growth of abnormal cells in the lobules or milk ducts. The treatment for the breast cancer at an early stage is important using Magnetic Resonance Imaging (MRI) which effectively measures the size of the cancer and also checks tumors in the opposite breast. The deposition of calcium components on the breast tissue is known as micro-calcifications. The calcium salts deposited in the breast are involved with the cancer and were not diagnosed accurately due to the low effectiveness of existing imaging technique namely Haralick feature extraction technique. The MRI breast cancer diagnosis creates problems during classification of breast image and leads to misclassifications, such as unidentified calcium deposits in the existing K-Nearest Neighbour (KNN) classifier. The misclassification issues are overcome by an accurate classification and identification of calcium salts and checks whether deposited salt on breast tissue is involved with cancer or not. Initially, Contrast-Limited Adaptive Histogram Equalization (CLAHE) is used to remove the unwanted noise in the MRI and Morphological, Multilevel Otsu’s Thresholding and region growing techniques perform segmentation to mask unwanted breast tissues. The proposed Hybrid LOOP Haralick feature extraction technique is developed by combining the both Local Optimal Oriented Pattern (LOOP) and Haralick texture feature and the hybrid parameters are applied to the Stacked Auto Encoder based (SAE) to classify the breast MRI image as a Malignant or Benign. The performance of the proposed hybrid LOOP Haralick feature extraction shows significant accuracy improvement of 3.83% when compared to the Haralick feature extraction technique.

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