Detection of Lung Cancer Disease Using Optimized Long Short-Term Memory Based on Improved Gray Wolf Optimization
Keywords:
long short-term memory; grey wolf optimization; opposition-based learning; local search algorithm; lung cancer; hyperparameter optimizationAbstract
Early diagnosis and reduced mortality rates are two benefits of early lung cancer detection in patients. This research proposes an optimized long short-term memory (LSTM) based on improved grey wolf optimization (IGWO) with opposition-based learning (OBL) and local search algorithm (LSA) as an effective lung cancer detection system. The OBL is utilized with GWO to boost its population diversity, and LSA is used with GWO to address its local optimum problem and improve the existing best solution. Then, the hyperparameters of the LSTM are optimized using IGWO, and the optimized LSTM is then used to detect lung cancer disease. The median filter is used to remove the unwanted noises from CT lung images, fuzzy c-means (FCM) is used to segment the affected regions, affected area is passed on to the feature extractions stage that extracts the various spectral features for effectively detecting lung disease. Finally, extracted features are considered as inputs to the optimized LSTM for detecting lung cancer diseases. High performance was achieved by the developed optimized method: 94.65% accuracy, 96.90% precision, 95.05% recall, and 94.94% f-measure. The experimental results revealed that the developed IGWO-LSTM achieved good detection accuracy and a quick convergence rate.
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