<![CDATA[Gallbladder diseases, including gallstones, carcinoma, and adenomyomatosis, may cause severe complications if not identified correctly and in a timely manner. However, ultrasound image interpretation relies heavily on operator experience and may suffer from subjectivity and inconsistency. This study aims to develop an automated and optimized classification model for gallbladder disease using ultrasound images, aiming to improve diagnostic reliability and efficiency. A key outcome of this research is a thorough assessment of how feature selection combined with hyperparameter tuning influences the accuracy of classical machine learning models that use features extracted via CNN-based feature extraction. The proposed pipeline enhances diagnostic accuracy while remaining computationally efficient. The method involves extracting deep features from ultrasound images using a pre-trained VGG16 CNN model. The features are subsequently reduced using the SelectKBest method through Univariate Feature Selection. Multiple popular classification models, specifically SVM, Random Forest, KNN, and Logistic Regression were tested using both original settings and adjusted hyperparameters through grid search. A complete evaluation of model performance was conducted using the test set, employing key performance indicators including overall prediction correctness (accuracy), actual positive rate (recall), positive prediction accuracy (precision), F1-score, and the ROC curve’s corresponding area value. Evaluation results suggest that the SVM approach, combined with selected features and hyperparameter tuning, achieved the highest performance: 99.35% accuracy, 99.32% precision, 99.35% recall, and 99.33% F1-score, with a relatively short computation time of 18.4 seconds. In conclusion, feature selection and hyperparameter tuning significantly enhance classification performance, making the proposed method a promising candidate for clinical decision support in gallbladder disease diagnosis using ultrasound imaging.]]>
