Abstract
Ovarian tumors do not really typically occur in association with pregnant; however, once they do, the treatment is critical. It is important to note that around 6% of ovarian tumors in pregnancies are cancerous. The problems induced by ovarian tumors in pregnancy particularly necessitate rapid medical intervention and are much more frequent than cancer. Medication choices and survival of ovary tumor patients could be influenced by varied diagnoses of ovarian masses. So, we present an upgraded logistic regression (ULR) approach in this paper. Initially, the collection of 137 patient datasets was employed in screening test to identify the ovarian tumor as benign-tumor and malignant-tumor by using contrast-enhanced ultrasonography (CEU) method. Then, the screening test images are preprocessed using wavelet transform (WT) approach. The preprocessed data are extracted by using local binary pattern (LBP) and laws' texture energy (LTE) techniques. Finally, the clinical analysis of the ovarian tumor can be obtained by the proposed ULR approach. The performances were examined and compared with existing approaches to achieve the proposed approach with greatest correctness. The findings are depicted by utilizing the MATLAB tool.