A Transfer Learning Approach for Bipolar Disease Detection

Abstract

Bipolar illness is a complicated mental health issue that affects a large section of the world. Effective bipolar illness therapy requires early and precise diagnosis. This work proposes a unique transfer learning strategy for bipolar disorder identification using many modules to improve prediction accuracy. The first module trains a CNN, BiLSTM, and RBF. Deep learning architectures help this module extract relevant characteristics from raw input data. The second module uses a DNN for feature selection to enhance feature representation. The DNN model eliminates superfluous or duplicated data by identifying the most important bipolar disorder diagnosis characteristics. In the third module, transfer learning uses a pre-trained model. Pre-trained models improve bipolar illness prediction by using learnt representations. Transfer learning is modified to include domain knowledge from related activities or datasets. The fourth module implements a RESNET Classification module. RESNET excels in picture categorization; therefore we use it to forecast bipolar disorder by capturing complicated data patterns and correlations. In the fifth module, SGD optimizes the model. By repeatedly adjusting parameters based on a portion of training data, SGD speeds convergence and improves accuracy. Finally, we optimize Levy Flight-based Fruit fly optimization to fine-tune model parameters. This technique optimizes hyper parameters including learning rate, batch size, and regularization for optimal bipolar illness identification.