Hybrid deep learning enabled breast cancer detection using mammogram images

Breast cancer (BC) is one of the most widespread kinds of cancer that affects women. Mammography is the most employed imaging modality for detecting BC. Earliest BC detection is a vital phase for effective treatment of disease. The mortality from BC can be lessened by detecting and recognizing it at an earlier stage. Here, Quantum SpinalNet (Q-SpinalNet) is introduced for detecting BC utilizing mammogram (MG) images. An input MG image is taken from a definite database initially and it is then pre-processed. Non -Local Means Filter (NLM) filter is employed for pre-processing an image. Thereafter, the segmentation of the images is carried out utilizing ETSegNet which is an integration of Edge-aTtention guidance Network (ET -Net) with Segmentation Network (SegNet). These two networks are fused based on the Random Variable (RV) coefficient. After that, features namely Local Ternary Pattern (LTP), Fuzzy Local Binary Patterns (FLBP), statistical features, Pyramid Histogram of Orientation Gradients (PHoG) and Median Binary Patterns are extracted. At last, BC detection is accomplished by Q-SpinalNet, which is designed by amalgamating Deep Quantum Neural Network (DQNN) with SpinalNet. Furthermore, Q-SpinalNet obtained 90.3% of accuracy, 90.9% of True Negative Rate (TNR) and 90% of True Positive Rate (TPR).