Computer-Aided Diagnosis of Various Diseases Using Ultrasonography Images.

This paper is an exhaustive survey of computer-aided diagnosis (CAD) system-based automatic detection of several diseases from ultrasound images. CAD plays a vital role in the automatic and early detection of diseases. Health monitoring, medical database management, and picture archiving systems became very feasible with CAD, assisting radiologists in making decisions over any imaging modality. Imaging modalities mainly rely on machine learning and deep learning algorithms for early and accurate disease detection. CAD approaches are described in this paper in terms of their significant tools; digital image processing (DIP), machine learning (ML), and deep learning (DL). Ultrasonography (USG) already has many advantages over other imaging modalities; therefore, CAD analysis of USG assists radiologists in studying it more clearly, leading to USG application over various body parts. So, in this paper, we have included a review of those major diseases whose detection supports "ML algorithm" based diagnosis from USG images. ML algorithm follows feature extraction, selection, and classification in the required class. The literature survey of these diseases is grouped into the carotid region, transabdominal & pelvic region, musculoskeletal region, and thyroid region. These regions also differ in the types of transducers employed for scanning. Based on the literature survey, we have concluded that texture-based extracted features passed to support vector machine (SVM) classifier results in good classification accuracy. However, the emerging deep learning-based disease classification trend signifies more preciseness and automation for feature extraction and classification. Still, classification accuracy depends on the number of images used for training the model. This motivated us to highlight some of the significant shortcomings of automated disease diagnosis techniques. Research challenges in CAD-based automatic diagnosis system design and limitations in imaging through USG modality are mentioned as separate topics in this paper, indicating future scope or improvement in this field. The success rate of machine learning approaches in USG-based automatic disease detection motivated this review paper to describe different parameters behind machine learning and deep learning algorithms towards improving USG diagnostic performance.