FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4377095242> ?p ?o ?g. }

• W4377095242 abstract "Purpose The objective of the proposed work is to identify the most commonly occurring non–small cell carcinoma types, such as adenocarcinoma and squamous cell carcinoma, within the human population. Another objective of the work is to reduce the false positive rate during the classification. Design/methodology/approach In this work, a hybrid method using convolutional neural networks (CNNs), extreme gradient boosting (XGBoost) and long-short-term memory networks (LSTMs) has been proposed to distinguish between lung adenocarcinoma and squamous cell carcinoma. To extract features from non–small cell lung carcinoma images, a three-layer convolution and three-layer max-pooling-based CNN is used. A few important features have been selected from the extracted features using the XGBoost algorithm as the optimal feature. Finally, LSTM has been used for the classification of carcinoma types. The accuracy of the proposed method is 99.57 per cent, and the false positive rate is 0.427 per cent. Findings The proposed CNN–XGBoost–LSTM hybrid method has significantly improved the results in distinguishing between adenocarcinoma and squamous cell carcinoma. The importance of the method can be outlined as follows: It has a very low false positive rate of 0.427 per cent. It has very high accuracy, i.e. 99.57 per cent. CNN-based features are providing accurate results in classifying lung carcinoma. It has the potential to serve as an assisting aid for doctors. Practical implications It can be used by doctors as a secondary tool for the analysis of non–small cell lung cancers. Social implications It can help rural doctors by sending the patients to specialized doctors for more analysis of lung cancer. Originality/value In this work, a hybrid method using CNN, XGBoost and LSTM has been proposed to distinguish between lung adenocarcinoma and squamous cell carcinoma. A three-layer convolution and three-layer max-pooling-based CNN is used to extract features from the non–small cell lung carcinoma images. A few important features have been selected from the extracted features using the XGBoost algorithm as the optimal feature. Finally, LSTM has been used for the classification of carcinoma types." @default.
• W4377095242 created "2023-05-20" @default.
• W4377095242 creator A5026262641 @default.
• W4377095242 creator A5043320615 @default.
• W4377095242 creator A5068965698 @default.
• W4377095242 creator A5074851678 @default.
• W4377095242 date "2023-05-19" @default.
• W4377095242 modified "2023-09-29" @default.
• W4377095242 title "A hybrid learning method for distinguishing lung adenocarcinoma and squamous cell carcinoma" @default.
• W4377095242 cites W1987054640 @default.
• W4377095242 cites W2083927153 @default.
• W4377095242 cites W2253429366 @default.
• W4377095242 cites W2594525001 @default.
• W4377095242 cites W2734776202 @default.
• W4377095242 cites W2796027894 @default.
• W4377095242 cites W2887808321 @default.
• W4377095242 cites W2894713578 @default.
• W4377095242 cites W2917837889 @default.
• W4377095242 cites W2930995728 @default.
• W4377095242 cites W2948353194 @default.
• W4377095242 cites W2954113789 @default.
• W4377095242 cites W2969626490 @default.
• W4377095242 cites W2981380606 @default.
• W4377095242 cites W2992717377 @default.
• W4377095242 cites W2997947674 @default.
• W4377095242 cites W3003376220 @default.
• W4377095242 cites W3006988488 @default.
• W4377095242 cites W3020959930 @default.
• W4377095242 cites W3022802582 @default.
• W4377095242 cites W3048216881 @default.
• W4377095242 cites W3089778393 @default.
• W4377095242 cites W3100355572 @default.
• W4377095242 cites W3102476541 @default.
• W4377095242 cites W3135485367 @default.
• W4377095242 cites W3168600003 @default.
• W4377095242 cites W4200046589 @default.
• W4377095242 cites W4200087356 @default.
• W4377095242 cites W4225998923 @default.
• W4377095242 cites W4226126985 @default.
• W4377095242 cites W4285741624 @default.
• W4377095242 doi "https://doi.org/10.1108/dta-10-2022-0384" @default.
• W4377095242 hasPublicationYear "2023" @default.
• W4377095242 type Work @default.
• W4377095242 citedByCount "0" @default.
• W4377095242 crossrefType "journal-article" @default.
• W4377095242 hasAuthorship W4377095242A5026262641 @default.
• W4377095242 hasAuthorship W4377095242A5043320615 @default.
• W4377095242 hasAuthorship W4377095242A5068965698 @default.
• W4377095242 hasAuthorship W4377095242A5074851678 @default.
• W4377095242 hasConcept C121608353 @default.
• W4377095242 hasConcept C126322002 @default.
• W4377095242 hasConcept C138885662 @default.
• W4377095242 hasConcept C142724271 @default.
• W4377095242 hasConcept C153180895 @default.
• W4377095242 hasConcept C154945302 @default.
• W4377095242 hasConcept C2776401178 @default.
• W4377095242 hasConcept C2777546739 @default.
• W4377095242 hasConcept C2778911148 @default.
• W4377095242 hasConcept C2781182431 @default.
• W4377095242 hasConcept C2908647359 @default.
• W4377095242 hasConcept C2994491419 @default.
• W4377095242 hasConcept C41008148 @default.
• W4377095242 hasConcept C41895202 @default.
• W4377095242 hasConcept C70437156 @default.
• W4377095242 hasConcept C71924100 @default.
• W4377095242 hasConcept C81363708 @default.
• W4377095242 hasConcept C99454951 @default.
• W4377095242 hasConceptScore W4377095242C121608353 @default.
• W4377095242 hasConceptScore W4377095242C126322002 @default.
• W4377095242 hasConceptScore W4377095242C138885662 @default.
• W4377095242 hasConceptScore W4377095242C142724271 @default.
• W4377095242 hasConceptScore W4377095242C153180895 @default.
• W4377095242 hasConceptScore W4377095242C154945302 @default.
• W4377095242 hasConceptScore W4377095242C2776401178 @default.
• W4377095242 hasConceptScore W4377095242C2777546739 @default.
• W4377095242 hasConceptScore W4377095242C2778911148 @default.
• W4377095242 hasConceptScore W4377095242C2781182431 @default.
• W4377095242 hasConceptScore W4377095242C2908647359 @default.
• W4377095242 hasConceptScore W4377095242C2994491419 @default.
• W4377095242 hasConceptScore W4377095242C41008148 @default.
• W4377095242 hasConceptScore W4377095242C41895202 @default.
• W4377095242 hasConceptScore W4377095242C70437156 @default.
• W4377095242 hasConceptScore W4377095242C71924100 @default.
• W4377095242 hasConceptScore W4377095242C81363708 @default.
• W4377095242 hasConceptScore W4377095242C99454951 @default.
• W4377095242 hasLocation W43770952421 @default.
• W4377095242 hasOpenAccess W4377095242 @default.
• W4377095242 hasPrimaryLocation W43770952421 @default.
• W4377095242 hasRelatedWork W1964681864 @default.
• W4377095242 hasRelatedWork W1995209464 @default.
• W4377095242 hasRelatedWork W2028501011 @default.
• W4377095242 hasRelatedWork W2475257193 @default.
• W4377095242 hasRelatedWork W2758063741 @default.
• W4377095242 hasRelatedWork W2760085659 @default.
• W4377095242 hasRelatedWork W2792080776 @default.
• W4377095242 hasRelatedWork W2940661641 @default.
• W4377095242 hasRelatedWork W2969680539 @default.
• W4377095242 hasRelatedWork W4283454150 @default.
• W4377095242 isParatext "false" @default.
• W4377095242 isRetracted "false" @default.

• next