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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.

Medical diagnostic systems has recently been very popular and reliable because of possible automatic detections. The machine learning algorithm is evolved as a core tool of computer-aided diagnosis (CAD) for automatic early and accurate disease detections. The algorithm follows region of interest (ROI) selection followed by specific feature extractions and selection from medical images. The selected features are then fed to suitable classifiers for disease identification. The machine learning algorithm's performance depends on the features selected and the classifiers employed for the job. This paper reviews different feature extraction selection and classification techniques for CAD from ultrasound images. Ultrasonography (USG), due to its portability and its non-invasive nature, is the prime choice of doctors for prescribing as an imaging test. A survey on the USG imaging based on four major diseases is performed in this paper, whose diagnosis followed by automatic detection. Various techniques applied for feature extraction, selection, and classification by different authors to achieve improved accuracy are tabulated. For medical images, we found texture based gray-level extracted features and SVM (support vector machine) classifiers to be more significant in improving classification accuracy, even achieving 100% accuracy in many research articles. However, many research articles also suggest the importance of student’s t-test in improving classification accuracy by selecting significant features from extracted features. The proposed algorithm's accuracy also depends on the quality of medical images, which are frequently degraded by the introduction of noise and artifacts while imaging acquisition. So, challenges in denoising are added in this paper as a separate topic to highlight the role of the machine learning algorithm in removing noise and artifacts from the USG images.

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