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Therefore, developing an autonomous, rapid, and reliable method of reading, detecting, and assessing CT scans is important. However, extracting the liver region from CT scans is a bottleneck for any approach. This paper introduces a three-part automatic process. Initial processing includes noise suppression and image enhancement. Optimized Bi-lateral Filtering is used to carry it out; in this case, the process's control parameters are optimized using the Monarch butterfly optimization method. After that, automatic liver segmentation and lesion identification are performed. Mask-Region-based Convolutional Neural Network segment liver from the pre-processed images. Then a new generator network named LiverNet is used to detect tumors within the liver. Finally, an Enhanced Swin Transformer Network employing Adversarial Propagation distinguishes between malignant and benign liver lesions. Positive developments were discovered as a result of the inquiry. Expert results are associated with the consequences of segmentation and analysis. The classifier makes a relatively accurate tumour differentiation and gives the radiologist a second opinion.<\/jats:p>","DOI":"10.1177\/1088467x241301660","type":"journal-article","created":{"date-parts":[[2025,2,6]],"date-time":"2025-02-06T01:50:02Z","timestamp":1738806602000},"page":"1289-1312","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["Livernet based segmentation of lesions from computed tomography scan for liver tumor detection"],"prefix":"10.1177","volume":"29","author":[{"given":"Priyan Malarvizhi","family":"Kumar","sequence":"first","affiliation":[{"name":"Department of Data Science, University of North Texas, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hardik","family":"Gohel","sequence":"additional","affiliation":[{"name":"University of Houston-Victoria, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jeeva","family":"Selvaraj","sequence":"additional","affiliation":[{"name":"Department of ISE, Jain University-Global Campus, Bangalore, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Balasubramanian Prabhu","family":"Kavin","sequence":"additional","affiliation":[{"name":"Department of Data Science and Business Systems, SRM Institute of Science and Technology, Chengalpattu, Tamil Nadu, India"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2025,2,5]]},"reference":[{"key":"e_1_3_3_2_2","first-page":"21","volume-title":"Automated detection and classification of liver cancer from CT images using HOG-SVM modelProceedings of the 2019 5th International Conference on Advances in Electrical Engineering (ICAEE 2019)","author":"Al Sadeque Z","unstructured":"Al Sadeque Z, Khan TI, Hossain QD, et al. Automated detection and classification of liver cancer from CT images using HOG-SVM model. In: Proceedings of the 2019 5th International Conference on Advances in Electrical Engineering (ICAEE 2019), Dhaka, Bangladesh, 26\u201328 September 2019, pp.21\u201326."},{"key":"e_1_3_3_3_2","first-page":"129","volume-title":"Lecture notes on data engineering and communications technologies","author":"Ba Alawi AE","year":"2021","unstructured":"Ba Alawi AE, Saeed AYA, Radman BMN, et al. A comparative study on liver tumor detection using CT images. In: Lecture notes on data engineering and communications technologies. 72. 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