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Moreover, there is no reliable contribution reported in the literature for diagnosing pelvic lesions from the pelvic portion of humans, especially females. While few contributions are found for diagnosis of lesions in the pelvic region, no effort has been made on enhancing the images. Inspired from the neural network (NN), our methodology adopts deep belief NN for enhancing the ultrasound image with pelvic lesions. The higher-order statistical characteristics of image textures, such as entropy and autocorrelation, are considered to enhance the image from its noisy environment. The alignment problem is considered using skewness. The proposed method is compared with the existing NN method to demonstrate its enhancement performance.<\/jats:p>","DOI":"10.1515\/jisys-2016-0112","type":"journal-article","created":{"date-parts":[[2017,6,2]],"date-time":"2017-06-02T09:19:28Z","timestamp":1496395168000},"page":"507-522","source":"Crossref","is-referenced-by-count":0,"title":["Deep Belief Network for the Enhancement of Ultrasound Images with Pelvic Lesions"],"prefix":"10.1515","volume":"27","author":[{"given":"Sadanand L.","family":"Shelgaonkar","sequence":"first","affiliation":[{"name":"Pacific Academy of Higher Education and Research University , Udaipur , Rajasthan, India"}]},{"given":"Anil B.","family":"Nandgaonkar","sequence":"additional","affiliation":[{"name":"Dr. Babasaheb Ambedkar Technological University , Lonere , Raigad , Maharashtra, India"}]}],"member":"374","published-online":{"date-parts":[[2017,5,20]]},"reference":[{"key":"2025120523275892441_j_jisys-2016-0112_ref_001_w2aab3b7b2b1b6b1ab1b6b1Aa","doi-asserted-by":"crossref","unstructured":"M. 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