Enhancement of Breast Cancer Screening through Texture and Deep Feature Fusion Model using MLO and CC View Mammograms

A common cancer subtype found in women with high mortality and occurrence rates is Breast Cancer (BC). BC ranks second among the causes of high mortality rates in women. The annual death rate due to breast cancer surpasses that of any other cancer type. The global survival rate for patients with breast cancer remains suboptimal. To enhance this survival rate, it is essential to implement intervention techniques for early detection and treatment. Screening using the Medio-Latera- -Oblique (MLO) view and the Cranio-Caudal (CC) view improved the detection of cancer signs in small lesions. This motivated the radiologist to use both mammographic views for screening and subsequently to acquire additional information. To automate this sequential screening process, Image Processing, and Artificial Intelligence (AI) techniques are incorporated into these views individually and their results were fused. Further, feature fusion from both views is analyzed by researchers to enhance the overall performance of the system. The proposed model is more concentrated on the extraction and fusion of deep features from the two views to improve screening efficacy. The effectiveness of the proposed workflow is assessed on mammogram images taken from the MLO view and CC views of the DDSM dataset. Medical imaging data in conjunction with Machine Learning (ML) methods are employed for breast cancer (BC) detection and classification, but they tend to be time-intensive. Leveraging Deep Learning (DL) algorithms has the potential to further enhance the detection accuracy.

This work focuses on improving the detection performance by using a fusion of texture and Resnet 50 deep feature of MLO and CC view mammograms followed by Support Vector Machine (SVM) classification. An improved accuracy of 98.1% is achieved when compared to existing works. Henceforth, this work can be employed for the early BC diagnosis.

Keywords: Breast cancer, Deep learning, Feature fusion, Detection, Classification, Accuracy, Mammogram, Texture, Deep features, Resnet 50.