Abstract
Breast Cancer is a common type of cancer diagnosed and it is a leading cause of death amongst the female population worldwide. Ultrasound imaging is the preferred method used by hospitals for detection of breast cancer, due to the fact that it is much safer that other imaging modalities. However, Ultrasound images are contaminated with noise that is non-Gaussian and multiplicative referred to as speckles. Currently, medical technicians and physicians do diagnosis of breast cancer by manually inspecting the ultrasound images, which makes the process time consuming and costly. This may be considered as an issue which prevents the early detection of breast cancer. Hence, an early diagnosis of breast cancer can be beneficial in not only prescribing medical procedure that inhibits the cancer from spreading but also in minimizing the fatality rate. Due to the Speckles (noise) in ultrasound, automatic detection and diagnosis is an extremely difficult task. In this paper, a Convolutional Neural Network (CNN) has been proposed for Despeckling (Denoising) the ultrasound images and afterwards another CNN model is proposed for the classification of the ultrasound images into benign and malignant classes. The proposed models are tested on a Mendeley Breast Ultrasound dataset. Experimental results indicate that a classification accuracy of 99.89% is achieved through the proposed model and that the proposed model(s) outperform other methods in proposed in recent studies.