BREAST ULTRASOUND IMAGE ENHANCEMENT TECHNIQUES FOR CANCER DETECTION AND CLASSIFICATION

Abstract

Breast cancer is the type of cancer with the highest incidence and global mortality of female cancers. Thus, the adaptation of modern technologies that assist in medical diagnosis in order to accelerate, automate and reduce the subjectivity of this process is of paramount importance for an efficient treatment. Therefore, this work aims to propose a platform to compare and evaluate the proposed strategies for improving breast ultrasound images and compare them with state-of- the-art techniques by classifying them as benign, malignant, and normal. Investigations were performed on a dataset containing a total of 780 images of tumor-affected people, divided into benign, malignant, and normal. A data augmentation technique was used to scale up the corpus of images available in the chosen dataset. For this, novel image enhancement techniques were used and the Multilayer Perceptrons, k-Nearest Neighbor, and Support Vector Machines algorithms were used for classification. From the promising outcomes of the conducted experiments, it was observed that the image enhancement algorithm called bilateral together with the SVM classifier achieved the best result for the classification of breast cancer, with an overall accuracy of 96.69% and an accuracy for the detection of malignant nodules of 95.11%. Therefore, it was found that the application of image enhancement methods can help in the detection of breast cancer at a much earlier stage with better accuracy in detection.