Clinical Value Analysis of Combined Vaginal Ultrasound, Magnetic Resonance Dispersion Weighted Imaging, and Multilayer Spiral CT in the Diagnosis of Endometrial Cancer Using Deep VGG-16 AdaBoost Hybrid Classifier

Abstract

Endometrial carcinoma is one of the most common disorders of the female reproductive system. Every year, around 76,000 women die from endometrial cancer around the world. Endometrial cancer is a significant factor in women’s health, particularly in industrialized nations, where the prevalence of this tumor type is the greatest. It is an important concern in women’s health because of disease mortality and the rising number of new diagnoses. The aim of the study was to investigate the clinical value of combined transvaginal ultrasound, magnetic resonance dispersion weighted imaging, and multilayer spiral computed tomography (CT) in the diagnosis of early-stage endometrial cancer. Initially, the dataset is collected that consisted of a total of 100 cases and split into the control group and experimental group of 50 cases in each group. The control group is diagnosed using conventional Doppler ultrasound diagnostic machine. The experimental group is diagnosed with combined ultrasound method. The ultrasound images thus obtained are preprocessed using the speckle-free adaptive wiener filter. The preprocessed images are segmented using the fuzzy clustering segmentation method. The features are extracted by the independent component analysis (ICA) method. We have proposed the deep VGG-16 AdaBoost hybrid classifier for classifying the normal and abnormal images. The clinical value of the diagnosis is analyzed using the parameters like diagnostic accuracy, specificity, sensitivity, and kappa coefficient. It is observed that the clinical value is better for the experimental group than the control group.

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“… Ashish et al [88] , Ogunleye et al [89] , Inan et al [90] Adaptive Boosting classifier It's an adaptive classifier that leverages the results of various weak learning algorithms to substantially enhance performance and provide an effective predictor for the boosted classifier's final output [91] Endometrial cancer prediction, Hepatitis disease detection, cancer classification etc. Wang et al [92] , Akbar et al [93] , Lu et al [94] Categorical Gradient (CAT) Boosting It is an implementation of Gradient Boost classifier that employs ordered boosting with categorical features and uses binary decision trees as underlying predictors [95] Parkinson's disease prediction, COVID-19 detection from blood samples, diabetes risk prediction etc. Al et al [96] , Abayomi et al [97] , Kumar et al [98] Boosting Ensemble ML classifiers: Boosting is an ensemble machine learning approach in which a random sample data is chosen, fitted with a model, and then trained in a sequential manner, combining a set of weak learners into a strong learner with an aim to minimize training errors, with every model attempting to compensate for the shortcomings of the previous model [101] .…”