Factorization-based active contour segmentation and pelican optimization-based modified bidirectional long short-term memory for ovarian tumor detection

This paper proposes a novel three-dimensional convolution neural network-based modified bidirectional long short-term memory with pelican optimization (3D CNN based MBiLSTM with PO) algorithm for multiclass ovarian tumor detection. Initially, the International Collaboration on Cancer Reporting endometrial cancer dataset images are provided in pre-processing phase, which uses a pre-emphasis filter to process the input image. In the segmentation phase, pre-processed data is then partitioned into diverse subgroups (i.e., pixels), which minimizes the complexity of images. In this paper, a factorization-based active contour technique is employed in the effective segmentation of images. The segmented features are then extracted and classified using the 3D CNN-MBiLSTM with PO algorithm. Finally, the experimental results are conducted and compared with various other approaches for various performance metrics. Each metric is evaluated with respect to the different number of iterations. The accuracy, sensitivity, and specificity have reached a higher value of 98.5%, 96%, and 98.25%, respectively.