Optimized Spatial Transformer for Segmenting Pancreas Abnormalities.
The precise delineation of the pancreas from clinical images poses a substantial obstacle in the realm of medical image analysis and surgical procedures. Challenges arise from the complexities of clinical image analysis and complications in clinical practice related to the pancreas. To tackle these...
| Publicado en: | Journal of Imaging Informatics in Medicine Vol. 38; no. 2; pp. 931 - 946 |
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| Autores principales: | , |
| Formato: | algorithm diagnostic images equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
Apr2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=184081731&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 184081731 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 29482925 NR3A jtl: Journal of Imaging Informatics in Medicine issn: 29482925 maglogo: N pubinfo: dt: Apr2025 vid: 38 iid: 2 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 184081731 184081731 184081731 10.1007/s10278-024-01224-5 184081731 ppf: 931 ppct: 15 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Optimized Spatial Transformer for Segmenting Pancreas Abnormalities. aug: au: Sridevi, Banavathu Jaidhan, B. John affil: https://ror.org/02bdf7k74 GITAM (Deemed to be University), 530045, Visakhapatnam, Andhra Pradesh, India sug: subj: Pancreas Abnormalities Pancreas Pathology Pancreas Radiography Magnetic Resonance Imaging Methods Image Interpretation, Computer Assisted Image Processing, Computer Assisted Detection Algorithms Human Forecasting Pancreatic Diseases Diagnosis Pancreatic Neoplasms Diagnosis Carcinoma, Ductal Diagnosis Image Retrieval Systems Predictive Value of Tests Comparative Studies Convolutional Neural Networks Models, Statistical False Negative Results False Positive Results Sensitivity and Specificity ab: The precise delineation of the pancreas from clinical images poses a substantial obstacle in the realm of medical image analysis and surgical procedures. Challenges arise from the complexities of clinical image analysis and complications in clinical practice related to the pancreas. To tackle these challenges, a novel approach called the Spatial Horned Lizard Attention Approach (SHLAM) has been developed. As a result, a preprocessing function has been developed to examine and eliminate noise barriers from the trained MRI data. Furthermore, an assessment of the current attributes is conducted, followed by the identification of essential elements for forecasting the impacted region. Once the affected region has been identified, the images undergo segmentation. Furthermore, it is crucial to emphasize that the present study assigns 80% of the data for training and 20% for testing purposes. The optimal parameters were assessed based on precision, accuracy, recall, F-measure, error rate, Dice, and Jaccard. The performance improvement has been demonstrated by validating the method on various existing models. The SHLAM method proposed demonstrated an accuracy rate of 99.6%, surpassing that of all alternative methods. pubtype: Academic Journal doctype: algorithm diagnostic images equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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