{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,8]],"date-time":"2026-01-08T21:29:32Z","timestamp":1767907772456,"version":"3.49.0"},"reference-count":42,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2020,7,9]],"date-time":"2020-07-09T00:00:00Z","timestamp":1594252800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["MAKE"],"abstract":"<jats:p>This research presents a machine vision approach to detect lesions in liver ultrasound as well as resolving some issues in ultrasound such as artifacts, speckle noise, and blurring effect. The anisotropic diffusion is modified using the edge preservation conditions which found better than traditional ones in quantitative evolution. To dig for more potential information, a learnable super-resolution (SR) is embedded into the deep CNN. The feature is fused using Gabor Wavelet Transform (GWT) and Local Binary Pattern (LBP) with a pre-trained deep CNN model. Moreover, we propose a Bayes rule-based informative patch selection approach to reduce the processing time with the selective image patches and design an algorithm to mark the lesion region from identified ultrasound image patches. To train this model, standard data ensures promising resolution. The testing phase considers generalized data with a varying resolution and test the performance of the model. Exploring cross-validation, it finds that a 5-fold strategy can successfully eradicate the overfitting problem. Experiment data are collected using 298 consecutive ultrasounds comprising 15,296 image patches. This proposed feature fusion technique confirms satisfactory performance compared to the current relevant works with an accuracy of 98.40%.<\/jats:p>","DOI":"10.3390\/make2030010","type":"journal-article","created":{"date-parts":[[2020,7,9]],"date-time":"2020-07-09T10:45:19Z","timestamp":1594291519000},"page":"172-191","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["Focal Liver Lesion Detection in Ultrasound Image Using Deep Feature Fusions and Super Resolution"],"prefix":"10.3390","volume":"2","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5905-6530","authenticated-orcid":false,"given":"Rafid","family":"Mostafiz","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, Mawlana Bhashani Science and Technology University, Santosh, Tangail 1902, Bangladesh"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4417-8276","authenticated-orcid":false,"given":"Mohammad","family":"Rahman","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Mawlana Bhashani Science and Technology University, Santosh, Tangail 1902, Bangladesh"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"A.","family":"Islam","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Georgia State University, Atlanta, GA 30302-5060, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Saeid","family":"Belkasim","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Georgia State University, Atlanta, GA 30302-5060, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,7,9]]},"reference":[{"key":"ref_1","unstructured":"(2020, April 16). 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