Automated lung cancer diagnosis using swarm intelligence with deep learning

Prediction of Breast Cancer Diseases From Genes Using Pso and Cso Tuned Long Short-term Memory

“…To improve accuracy in prediction of prostate cancer, the study employs LSTM recurrent neural network (RNN) and creates Time Series Radiomics (TSR) predictive model [7]. Ensemble classifier approach fine-tunes the critical parameters in each layers of Network [8].Hybrid reduction technique is used to optimizes the process for breast cancer diagnosis [9].Fuzzy C-Means Clustering is applied to optimizes the parameters in RNN through CSO for lung cancer detection [10].A non-invasive breast cancer classification system for Metastatic Breast Cancer (MBC) diagnosis was proposed through ML models using blood profile data of MBC patients for survival prediction [11].Deep Optimal Neurocomputing Technique through Multivariate Analysis is used to predict the cancer which reduces the computation time [12].A innovative technique, termed as self-adaptive sea lion optimization algorithm is used to optimize the weights using cutting-edge meta-heuristic algorithm [13].Adaboost and ensemble machine learning technique predicts early lung cancer. Adaboost proved less sensitive to training set and less prone to overfitting, valuable tool for early lung cancer diagnosis in clinical practice [14].…”

“…The hidden state is obtained using the mathematical formula as in equation ( 9). g_n = o_n * tanh(D_n) (10) The equations represent the core operations of an LSTM cell, allows to process sequential data and capture long-term dependencies. LSTM networks consist of multiple LSTM cells stacked together, enables them to model complex sequences effectively for tasks like time series forecasting.…”