A Tuned Whale Optimization-Based Stacked-LSTM Network for Digital Image Segmentation

Image segmentation refers to analysis of the images to understand its content, and now it is a very promising field of image processing and computer vision. The key objective of this study is to acquire optimal boundary lines to obtain the segmented images from heterogeneous datasets by tuning few relevant parameters of whale optimization algorithm (WOA) with long short-term memory (LSTM) network for heterogeneous datasets. In this paper, the LSTM has been explored to overcome the vanishing gradient problem of recurrent neural network (RNN) and the usefulness of stacked form of LSTM has been used to explore the depth of LSTM network which are more important than the number of memory cells given in a layer to model the skill. This study has been experimented with two LSTMs in a stacked form which provides a sequence output rather than a single value output to the LSTM layer below. Specifically, one output per input time step, rather than one output time step for all input time steps, is used to obtain the optimal and finer boundaries. The proposed tuned WOA hybridized with stacked form of LSTM for digital image segmentation strategy has been coined as S-LSTM-TWOA. The performance of the proposed model has been compared with hybridized S-LSTM network with genetic algorithm, particle swarm optimization, WOA and binary WOA techniques. The execution performance with respect to convergence speed, accuracy, execution time, Dice coefficient, intersection over union (IoU) is also recorded. The overall performance shows the segmentation capability of the proposed S-SLTM-TWOA to be more promising than the compared model by achieving approximately 99%, 99% and 98% for satellite images, BCN 20,000 and RVC 2020 datasets.