SVMEnabled Intelligent Genetic Algorithmic Model for Realizing Efficient Universal Feature Selection in Breast Cyst Image Acquired via Ultrasound Sensing Systems In recent years, there are several costeffective intelligent sensing systems such as ultrasound imaging systems for visualizing the internal body structures of the body. Further, such intelligent sensing systems such as ultrasound systems have been deployed by medical doctors around the globe for efficient detection of several diseases and disorders in the human body. Even though the ultrasound sensing system is a useful tool for obtaining the imagery of various body parts, there is always a possibility of inconsistencies in these images due to the variation in the settings of the system parameters. Therefore, in order to overcome such issues, this research devises an SVMenabled intelligent genetic algorithmic model for choosing the universal features with four distinct settings of the parameters. Subsequently, the distinguishing characteristics of these features are assessed utilizing the SorensenDice coefficient, Ttest, and Pearsonxe2x80x99s R measure. It is apparent from the results of the SVMenabled intelligent genetic algorithmic model that this approach aids in the effectual selection of universal features for the breast cyst images. In addition, this approach also accomplishes superior accuracy in the classification of the ultrasound image for four distinct settings of the parameters.

Predicting the Semantic Characteristics of Pulmonary Nodules using Feature Selection Based on Maximumrelevance Minimumredundancy Computeraided diagnosis (CAD) is mainly used in disease diagnosis and cause analysis. For example, using CAD to make early predictions of the semantic features of lung nodules is critical for helping physicians judge the semantic features of solitary pulmonary nodules. It is an effective method to predict disease using the features calculated from CT images. But how to select the most relevant features from the large number of image features is still a challenge. In this paper, we perform feature selection using maximumrelevance minimumredundancy criteria based on applying a support vector machine (MRMR_SVM) on four types of computed image features to predict the semantic characteristics of pulmonary nodules over seven categories. The proposed method has the following advantages. 1) It improves work efficiency and reduces costs compared to manual evaluation by radiologists. 2) It combines a few key image features with specific semantic features to provide a basis for radiologistsu0027 diagnosis. 3) It eliminates noisy data and improves accuracy of early prediction of pulmonary nodules compared to using all features. The experimental results show that the proposed method performs well at predicting the semantic features of lung nodules in terms of accuracy and running time.