IGWO-IVNet3: DL-Based Automatic Diagnosis of Lung Nodules Using an Improved Gray Wolf Optimization and InceptionNet-V3
DC Field | Value | Language |
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dc.contributor.author | Bilal, Anas | - |
dc.contributor.author | Shafiq, Muhammad | - |
dc.contributor.author | Fang, Fang | - |
dc.contributor.author | Waqar, Muhammad | - |
dc.contributor.author | Ullah, Inam | - |
dc.contributor.author | Ghadi, Yazeed Yasin | - |
dc.contributor.author | Long, Haixia | - |
dc.contributor.author | Zeng, Rao | - |
dc.date.accessioned | 2023-07-12T01:41:19Z | - |
dc.date.available | 2023-07-12T01:41:19Z | - |
dc.date.created | 2023-07-12 | - |
dc.date.issued | 2022-12-01 | - |
dc.identifier.issn | 1424-8220 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/88473 | - |
dc.description.abstract | Artificial intelligence plays an essential role in diagnosing lung cancer. Lung cancer is notoriously difficult to diagnose until it has progressed to a late stage, making it a leading cause of cancer-related mortality. Lung cancer is fatal if not treated early, making this a significant issue. Initial diagnosis of malignant nodules is often made using chest radiography (X-ray) and computed tomography (CT) scans; nevertheless, the possibility of benign nodules leads to wrong choices. In their first phases, benign and malignant nodules seem very similar. Additionally, radiologists have a hard time viewing and categorizing lung abnormalities. Lung cancer screenings performed by radiologists are often performed with the use of computer-aided diagnostic technologies. Computer scientists have presented many methods for identifying lung cancer in recent years. Low-quality images compromise the segmentation process, rendering traditional lung cancer prediction algorithms inaccurate. This article suggests a highly effective strategy for identifying and categorizing lung cancer. Noise in the pictures was reduced using a weighted filter, and the improved Gray Wolf Optimization method was performed before segmentation with watershed modification and dilation operations. We used InceptionNet-V3 to classify lung cancer into three groups, and it performed well compared to prior studies: 98.96% accuracy, 94.74% specificity, as well as 100% sensitivity. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | MDPI | - |
dc.relation.isPartOf | SENSORS | - |
dc.title | IGWO-IVNet3: DL-Based Automatic Diagnosis of Lung Nodules Using an Improved Gray Wolf Optimization and InceptionNet-V3 | - |
dc.type | Article | - |
dc.type.rims | ART | - |
dc.description.journalClass | 1 | - |
dc.identifier.wosid | 000902913300001 | - |
dc.identifier.doi | 10.3390/s22249603 | - |
dc.identifier.bibliographicCitation | SENSORS, v.22, no.24 | - |
dc.description.isOpenAccess | Y | - |
dc.identifier.scopusid | 2-s2.0-85144500973 | - |
dc.citation.title | SENSORS | - |
dc.citation.volume | 22 | - |
dc.citation.number | 24 | - |
dc.contributor.affiliatedAuthor | Ullah, Inam | - |
dc.type.docType | Article | - |
dc.subject.keywordAuthor | deep learning | - |
dc.subject.keywordAuthor | medical image diagnosis | - |
dc.subject.keywordAuthor | lung cancer | - |
dc.subject.keywordAuthor | computed tomography (CT) | - |
dc.subject.keywordAuthor | computer-aided diagnostic system (CAD) | - |
dc.subject.keywordAuthor | gray wolf optimization (GWO) | - |
dc.subject.keywordAuthor | genetic algorithm (GA) | - |
dc.subject.keywordAuthor | transfer learning | - |
dc.subject.keywordAuthor | classification | - |
dc.subject.keywordAuthor | segmentation | - |
dc.subject.keywordPlus | CONVOLUTIONAL NEURAL-NETWORK | - |
dc.subject.keywordPlus | CLASSIFICATION | - |
dc.subject.keywordPlus | SELECTION | - |
dc.subject.keywordPlus | VECTORS | - |
dc.subject.keywordPlus | DISEASE | - |
dc.relation.journalResearchArea | Chemistry | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalResearchArea | Instruments & Instrumentation | - |
dc.relation.journalWebOfScienceCategory | Chemistry, Analytical | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.relation.journalWebOfScienceCategory | Instruments & Instrumentation | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
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