{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,17]],"date-time":"2026-06-17T09:56:27Z","timestamp":1781690187283,"version":"3.54.5"},"reference-count":38,"publisher":"Wiley","issue":"7","license":[{"start":{"date-parts":[[2026,6,5]],"date-time":"2026-06-05T00:00:00Z","timestamp":1780617600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,6,5]],"date-time":"2026-06-05T00:00:00Z","timestamp":1780617600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Expert Systems"],"published-print":{"date-parts":[[2026,7]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>Freshness is a core quality indicator that determines the utilisation and commercial value of fish products. Traditional fish freshness detection methods are highly subjective and destructive, while existing neural network models suffer from low detection accuracy, unsatisfactory recall and confidence scores and limited generalisation ability. To address these limitations, this paper proposes a novel Dual\u2010Sensitive Convolutional Neural Network (S\u2010CNN), where the letter \u2018S\u2019 stands for Sensitive. The model simultaneously extracts and fuses discriminative features from fish eye and gill images, capturing subtle freshness differences through a dual\u2010sensitive feature extraction mechanism. In data preprocessing, all image pixels are normalised to the range [0, 1] to unify numerical scales, stabilise gradient descent and mitigate overfitting. The proposed S\u2010CNN is composed of seven convolutional blocks, each equipped with batch normalisation, L2 regularisation and a pooling layer; the pooling operation is omitted in the last block to avoid excessive dimensionality reduction. After the flatten layer, a Dropout regularisation module is adopted, and L2 regularisation is applied to all convolutional and fully connected layers. The network uses categorical crossentropy as the loss function. Experimental results demonstrate that the S\u2010CNN achieves a detection accuracy of 98.70% and an average confidence score of 99.18% on the fish freshness dataset, outperforming other comparative models. The results confirm that the fusion of fish eye and gill features can effectively evaluate fish freshness, providing a reliable method for nondestructive detection and quality assessment of fish products.<\/jats:p>","DOI":"10.1111\/exsy.70319","type":"journal-article","created":{"date-parts":[[2026,6,6]],"date-time":"2026-06-06T01:40:18Z","timestamp":1780710018000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["S\u2010\n                    <scp>CNN<\/scp>\n                    : A Dual\u2010Region Feature Convolutional Network for Fish Freshness Assessment Based on Eyes and Gills Characteristics"],"prefix":"10.1111","volume":"43","author":[{"given":"Boqi","family":"Suzhang","sequence":"first","affiliation":[{"name":"School of Information Science and Engineering, Dalian Polytechnic University  Dalian China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaozhou","family":"He","sequence":"additional","affiliation":[{"name":"Network Information Center Dalian Medical University  Dalian China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xianying","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering, Dalian Polytechnic University  Dalian China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xuhui","family":"Huang","sequence":"additional","affiliation":[{"name":"SKL of Marine Food Processing and Safety Control, National Engineering Research Center of Seafood, Collaborative Innovation Center of Seafood Deep Processing School of Food Science and Technology, Dalian Polytechnic University  Dalian China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3902-3192","authenticated-orcid":false,"given":"Lei","family":"Qin","sequence":"additional","affiliation":[{"name":"SKL of Marine Food Processing and Safety Control, National Engineering Research Center of Seafood, Collaborative Innovation Center of Seafood Deep Processing School of Food Science and Technology, Dalian Polytechnic University  Dalian China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5790-7429","authenticated-orcid":false,"given":"Suo","family":"Gao","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering, Dalian Polytechnic University  Dalian China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jun","family":"Mou","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering, Dalian Polytechnic University  Dalian China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ahmed A. Abd","family":"El\u2010Latif","sequence":"additional","affiliation":[{"name":"EIAS Data Science Lab, College of Computer and Information Sciences, and Center of Excellence in Quantum and Intelligent Computing Prince Sultan University  Riyadh Saudi Arabia"},{"name":"Department of Mathematics and Computer Science, Faculty of Science Menoufia University  Shebin El\u2010Koom Egypt"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Basma Abd","family":"El\u2010Rahiem","sequence":"additional","affiliation":[{"name":"Department of Mathematics and Computer Science, Faculty of Science Menoufia University  Shebin El\u2010Koom 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