{"title":"基于Adam秃鹰优化的Shepard CNN在颅脑MRI术后和术前肿瘤分类和像素变化检测中的应用","authors":"S Abirami, B Lanitha","doi":"10.1080/13682199.2023.2262259","DOIUrl":null,"url":null,"abstract":"ABSTRACTBrain tumour is a dangerous disease and it harms health. This research develops a productive model to categorize brain tumours exploiting an Adam Bald Eagle optimization-based Shepard Convolutional Neural Network (ABEO-ShCNN). Initially, the preprocessing is done in pre- and post-operative Magnetic resonance imaging (MRI). Then, U-Net++ is exploited to segment, which is tuned by the Bald Border Collie Firefly Optimization Algorithm (BBCFO). The BBCFO is the incorporation of Border Collie Optimization (BCO), the Firefly optimization Algorithm (FA) and Bald Eagle Search (BES). Thereafter, feature extraction is done and then categorization is conducted using ShCNN in which the training is conducted by ABEO. The ABEO is the integration of Adam and BES. The ABEO-ShCNN model has acquired better accuracy, Positive Predictive Value (PPV), True Negative Rate (TNR), True Positive Rate (TPR) and Negative Predictive Value (NPV) for pre-operative MRI, with values of 92.70%, 92.90%, 91.30%, 89.60% and 89.50%, respectively.KEYWORDS: Shepard convolutional neural networkbald eagle search algorithmborder collie optimizationmagnetic resonance imagingfirefly optimization algorithmU-Net++Shepard Convolutional Neural Networkfeature extraction Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationNotes on contributorsS AbiramiMrs. Abirami S obtained her Bachelor and Master's degrees in computer science and engineering from Anna University, Chennai in 2005 and 2013, respectively. She has worked in various reputed engineering institutions and software industries in and around India. Currently, she is working as an assistant professor in the department of computer science and engineering at Sri Krishna College of Engineering and Technology in Coimbatore, Tamilnadu, India. Her area of interest is Machine Learning and Deep learning.B LanithaDr. Lanitha B received her Bachelor and Master's degrees in computer science and engineering from Bharathiyar University and Karpagam University in 1989 and 1993, respectively. She earned her Ph.D. at Anna University in 2021. Currently, she is working as an associate professor at Karpagam Academy of Higher Education. She has worked in various reputed engineering institutions and software industries. She has published many papers in international journals and conferences.","PeriodicalId":22456,"journal":{"name":"The Imaging Science Journal","volume":"15 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Adam bald eagle optimization-based Shepard CNN for classification and pixel change detection of brain tumour using post and pre-operative brain MRI images\",\"authors\":\"S Abirami, B Lanitha\",\"doi\":\"10.1080/13682199.2023.2262259\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"ABSTRACTBrain tumour is a dangerous disease and it harms health. This research develops a productive model to categorize brain tumours exploiting an Adam Bald Eagle optimization-based Shepard Convolutional Neural Network (ABEO-ShCNN). Initially, the preprocessing is done in pre- and post-operative Magnetic resonance imaging (MRI). Then, U-Net++ is exploited to segment, which is tuned by the Bald Border Collie Firefly Optimization Algorithm (BBCFO). The BBCFO is the incorporation of Border Collie Optimization (BCO), the Firefly optimization Algorithm (FA) and Bald Eagle Search (BES). Thereafter, feature extraction is done and then categorization is conducted using ShCNN in which the training is conducted by ABEO. The ABEO is the integration of Adam and BES. The ABEO-ShCNN model has acquired better accuracy, Positive Predictive Value (PPV), True Negative Rate (TNR), True Positive Rate (TPR) and Negative Predictive Value (NPV) for pre-operative MRI, with values of 92.70%, 92.90%, 91.30%, 89.60% and 89.50%, respectively.KEYWORDS: Shepard convolutional neural networkbald eagle search algorithmborder collie optimizationmagnetic resonance imagingfirefly optimization algorithmU-Net++Shepard Convolutional Neural Networkfeature extraction Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationNotes on contributorsS AbiramiMrs. Abirami S obtained her Bachelor and Master's degrees in computer science and engineering from Anna University, Chennai in 2005 and 2013, respectively. She has worked in various reputed engineering institutions and software industries in and around India. Currently, she is working as an assistant professor in the department of computer science and engineering at Sri Krishna College of Engineering and Technology in Coimbatore, Tamilnadu, India. Her area of interest is Machine Learning and Deep learning.B LanithaDr. Lanitha B received her Bachelor and Master's degrees in computer science and engineering from Bharathiyar University and Karpagam University in 1989 and 1993, respectively. She earned her Ph.D. at Anna University in 2021. Currently, she is working as an associate professor at Karpagam Academy of Higher Education. She has worked in various reputed engineering institutions and software industries. 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Adam bald eagle optimization-based Shepard CNN for classification and pixel change detection of brain tumour using post and pre-operative brain MRI images
ABSTRACTBrain tumour is a dangerous disease and it harms health. This research develops a productive model to categorize brain tumours exploiting an Adam Bald Eagle optimization-based Shepard Convolutional Neural Network (ABEO-ShCNN). Initially, the preprocessing is done in pre- and post-operative Magnetic resonance imaging (MRI). Then, U-Net++ is exploited to segment, which is tuned by the Bald Border Collie Firefly Optimization Algorithm (BBCFO). The BBCFO is the incorporation of Border Collie Optimization (BCO), the Firefly optimization Algorithm (FA) and Bald Eagle Search (BES). Thereafter, feature extraction is done and then categorization is conducted using ShCNN in which the training is conducted by ABEO. The ABEO is the integration of Adam and BES. The ABEO-ShCNN model has acquired better accuracy, Positive Predictive Value (PPV), True Negative Rate (TNR), True Positive Rate (TPR) and Negative Predictive Value (NPV) for pre-operative MRI, with values of 92.70%, 92.90%, 91.30%, 89.60% and 89.50%, respectively.KEYWORDS: Shepard convolutional neural networkbald eagle search algorithmborder collie optimizationmagnetic resonance imagingfirefly optimization algorithmU-Net++Shepard Convolutional Neural Networkfeature extraction Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationNotes on contributorsS AbiramiMrs. Abirami S obtained her Bachelor and Master's degrees in computer science and engineering from Anna University, Chennai in 2005 and 2013, respectively. She has worked in various reputed engineering institutions and software industries in and around India. Currently, she is working as an assistant professor in the department of computer science and engineering at Sri Krishna College of Engineering and Technology in Coimbatore, Tamilnadu, India. Her area of interest is Machine Learning and Deep learning.B LanithaDr. Lanitha B received her Bachelor and Master's degrees in computer science and engineering from Bharathiyar University and Karpagam University in 1989 and 1993, respectively. She earned her Ph.D. at Anna University in 2021. Currently, she is working as an associate professor at Karpagam Academy of Higher Education. She has worked in various reputed engineering institutions and software industries. She has published many papers in international journals and conferences.