LSTM based Modified Remora Optimization Algorithm for Lung Cancer Prediction

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 (EGOA-RF) and obtained remarkable results with accuracy, precision, recall, and f-measure values of 99.02%, 99.17%, 99.03%, and 99.24% respectively.