التفاصيل البيبلوغرافية
| العنوان: |
LSTM based Modified Remora Optimization Algorithm for Lung Cancer Prediction. |
| المؤلفون: |
Pradhan, Manaswini, Coman, Ioana L., Mishra, Subhankar, Thieu, Thanh, Bhuiyan, Alauddin |
| المصدر: |
International Journal of Intelligent Engineering & Systems; 2023, Vol. 16 Issue 6, p46-59, 14p |
| مصطلحات موضوعية: |
OPTIMIZATION algorithms, CONVOLUTIONAL neural networks, LUNG cancer, FEATURE extraction, EARLY detection of cancer, RANDOM forest algorithms, PARTICLE swarm optimization |
| مستخلص: |
Early detection of lung cancer in patients can decrease the mortality rate and helps in early diagnosis. An efficient lung cancer prediction system is proposed in this paper known as a long short-term memory (LSTM) based modified remora optimization algorithm (MROA) with multi-objective criteria. Although the MRO is proposed by several researchers, it was based on single-objective with traditional objective functions which tried to maximize the accuracy. This led to the biased classifier with high accuracy and sacrificed sensitivity which resulted in insufficiency of one class and prevalence of other class. To overcome this limitation, a Multi-objective MROA (MMROA) for hyperparameter tuning is proposed, where both accuracy and sensitivity are equally considered as a fitness function. Initially the histopathology images of Lung and Colon 25000 (LC25000) dataset is normalised using colour normalization and segmented with saliency driven edge dependent top-down level set (SDREL) method. The features are extracted using Grey Level Cooccurrence-Matrix (GLCM) and GoogleNet followed by the feature selection using enhanced grasshopper optimization algorithm (EGOA). The selected features are optimized (hyperparameter tuning) using MMROA and fed to LSTM classifier. The proposed model is compared with the existing models such as convolutional neural network (CNN) and enhanced grasshopper optimization algorithm based random forest (EGOARF) and obtained remarkable results with accuracy, precision, recall, and f-measure values of 99.02%, 99.17%, 99.03%, and 99.24% respectively. [ABSTRACT FROM AUTHOR] |
|
Copyright of International Journal of Intelligent Engineering & Systems is the property of Intelligent Networks & Systems Society and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) |
| قاعدة البيانات: |
Complementary Index |
