Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Conference Papers Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Analysis of Kidney Ultrasound Images Using Deep Learning and Machine Learning Techniques: A Review Subject The topic of the resource Classification; Deep learning; Image processing; Kidney ultrasound images; Machine learning; Noise removal; Segmentation; Ultrasound (US) Description An account of the resource Ultrasonography is the most accepted and widely used imaging technique due to its non-invasive and radiation-free nature. The heterogeneous structure of kidney makes the disease detection a difficult task. Hence, more efficient models and methods are required to assist radiologists in making precise decisions. Since ultrasound imaging is considered to be the initial step in the diagnosis, more efficient processing techniques are needed in the interpretation of images. The presence of speckle noise is a challenge task in image processing. It diminishes the clarity of the images. In this article, an in-depth review has been performed on various machine learning and deep learning techniques, which are helping to improve the quality of images. The pre-processing, segmentation, feature extraction, and classification are described in detail using kidney cyst, stone, tumor, and normal kidney images. Deep learning techniques are enhancing the quality of the images with better accuracy. The remaining challenges and directions for future research are also explored. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. Creator An entity primarily responsible for making the resource George M.; Anita H.B. Source A related resource from which the described resource is derived Lecture Notes in Networks and Systems, Vol-317, pp. 183-199. Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Date A point or period of time associated with an event in the lifecycle of the resource 2022-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-16-5640-8_15" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-16-5640-8_15</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85122580531&amp;doi=10.1007%2F978-981-16-5640-8_15&amp;partnerID=40&amp;md5=188a1683afa8965746ab47023d92d8ef" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85122580531&amp;doi=10.1007%2f978-981-16-5640-8_15&amp;partnerID=40&amp;md5=188a1683afa8965746ab47023d92d8ef</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 23673370; ISBN: 978-981165639-2 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Conference paper Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant George M., Department of Computer Science, CHRIST (Deemed To Be University), Bengaluru, India; Anita H.B., Department of Computer Science, CHRIST (Deemed To Be University), Bengaluru, India