Abstract
Medical imaging techniques have widely been in use in the diagnosis and detection of breast cancer. The drawback of applying these techniques is the large time consumption in the manual diagnosis of each image pattern by a professional radiologist. Automated classifiers could substantially upgrade the diagnosis process, in terms of both accuracy and time requirement by distinguishing benign and malignant patterns automatically. Neural network (NN) plays an important role in this respect, especially in the application of breast cancer detection. Despite the large number of publications that describe the utilization of NN in various medical techniques, only a few reviews are available that guide the development of these algorithms to enhance the detection techniques with respect to specificity and sensitivity. The purpose of this review is to analyze the contents of recently published literature with special attention to techniques and states of the art of NN in medical imaging. We discuss the usage of NN in four different medical imaging applications to show that NN is not restricted to few areas of medicine. Types of NN used, along with the various types of feeding data, have been reviewed. We also address hybrid NN adaptation in breast cancer detection.
Highlights
Breast cancer is one of the main causes of death among women and the most frequently diagnosed non-skin cancer in women [1]
Various artificial neural network (ANN) developed are based on the concept of increasing the true positive (TP) detection rate and decreasing the false positive (FP) and false negative (FN) detection rate for the optimum result
Implementation of wavelet in ANNs such as Particle Swarm Optimized Wavelet Neural Network (PSOWNN), biorthogonal spline wavelet ANN, second-order grey-level ANN, and Gabor wavelets ANN can improve the sensitivity and specificity which are acquired in masses and microcalcification detection
Summary
Breast cancer is one of the main causes of death among women and the most frequently diagnosed non-skin cancer in women [1]. Breast cancer occurs when the cell tissues of the breast become abnormal and uncontrollably divided. These abnormal cells form large lump of tissues, which becomes a tumor [2]. Such disorders could successfully be treated if they are detected early. Microcalcifications and masses are the earliest signs of breast cancer which can only be detected using modern techniques. Detection of masses in breast tissues is more challenging compared to the detection of microcalcifications, due to the large variation in size and shape and because masses often exhibit poor image contrast when using mammography [3]. The difficulty in classification of benign and malignant microcalcifications causes a significant problem in medical image processing
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