Accurate Diagnosis of Diseases by a Novel AI Pipeline Based on Feature Extraction, Feature Ranking, and Feature Selection from Medical Images

The rapid growth of the global population has led to a substantial increase in the number of patients, while the availability of healthcare professionals has not expanded at a comparable rate. This imbalance highlights the urgent need for efficient and reliable computer-aided decision support systems that can reduce clinical workload while maintaining high diagnostic accuracy. In this study, a novel and systematically integrated artificial intelligence-based pipeline is proposed for medical image classification, combining statistical significance-driven feature ranking with evolutionary feature selection in a unified framework. The proposed pipeline consists of four sequential stages: feature extraction, ranking, selection, and classification. Features are extracted using pretrained AlexNet, ResNet-101, and GoogleNet architectures without requiring any network retraining. To ensure statistically grounded dimensionality reduction, individual features are ranked using two-sample z-test and one-way ANOVA, retaining only statistically significant features. Subsequently, nonlinear feature dependencies are captured through evolutionary optimization using binary genetic algorithm, binary differential evolution, and binary artificial bee colony (BABC) algorithms. The selected features are finally classified using k-nearest neighbors, support vector machines (SVM), and Na & iuml;ve Bayes classifiers under 4-fold cross-validation. The effectiveness of the pipeline is validated on three medical imaging datasets: chest X-ray, skin lesion, and breast ultrasound images. Results demonstrate that the AlexNet/ResNet-101-ANOVA-BABC-SVM configuration consistently outperforms alternative pipelines and baseline approaches. The proposed method achieves accuracies of 96.89%, 88.89%, and 92.71% on the respective datasets, while reducing the feature dimensionality to only 5-9% of the original feature space. Statistical analysis confirms that the performance improvements are significant compared to using only-features, features+ANOVA, features+BABC, and transfer learning-based models (p < 0.05). These results indicate that the proposed pipeline offers a statistically principled, computationally efficient, and highly generalizable alternative to end-to-end deep learning, providing a practical and robust decision support solution for medical image diagnosis across diverse imaging modalities.