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• W4386092707 endingPage "2027" @default.
• W4386092707 startingPage "1995" @default.
• W4386092707 abstract "The global population nowadays is severely affected by various diseases of the soft tissue organs caused mainly because of some infection, heredity, change of lifestyle, etc. The timely detection and accurate diagnosis of these health conditions are of utmost importance in order to improve the chances of recovery and survival. Many medical imaging modalities have proven to effectively diagnose these diseases and their progression in a noninvasive way. Out of all the available modalities, ultrasound is the preferred mode of examination for imaging soft tissue organs for disease prediction because of its ease of use, low cost, portability, and lack of ionizing radiation. The radiologists manually study these scans for making an accurate diagnosis of the underlying condition. However, ultrasound imaging is highly operator-dependent and its effectiveness is adversely affected because of the presence of speckle noise. Therefore, to overcome these issues and for an efficient disease diagnosis, different computer-aided diagnostic (CAD) systems have been developed by researchers using artificial intelligence techniques along with the metrological characteristics of the diseased part as visible on the ultrasound scan of the organ under study. The main purpose of the present chapter is to shed light on the different types of diseases that affect the soft tissue organs like kidney, liver, thyroid, breast and their sonographic appearances, and characteristics. The chapter further describes methodologies developed in recent existing literature (year 2017 onwards) for the classification of diseases using ultrasound images of these organs through a CAD system using state-of-the-art deep learning and machine learning methods. The chapter also gives an insight into designing an efficient CAD system for the classification of breast tumors. The authors in the chapter have used nonsubsampled contourlet transform (NSCT) for multiresolution analysis of the original tumor images. From the subimages obtained using NSCT, extraction of texture features has been carried out using gray level co-occurrence matrix (GLCM), whereas shape features have been computed from the preprocessed tumor images. The computed feature set (texture + shape) has been used for classifying breast tumors using an adaptive neuro-fuzzy classifier with linguistic hedges (ANFC-LH) classifier based on the optimal features selected on the basis of the hedge values associated with the fuzzy rules." @default.
• W4386092707 created "2023-08-24" @default.
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• W4386092707 date "2023-01-01" @default.
• W4386092707 modified "2023-10-01" @default.
• W4386092707 title "Use of Metrological Characteristics in Ultrasound Imaging and Artificial Intelligence Techniques for Disease Prediction in Soft Tissue Organs" @default.
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• W4386092707 doi "https://doi.org/10.1007/978-981-99-2074-7_132" @default.
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