WTBAN-CNN-TL: Wavelet Transform-Based Feature Extraction and Bonferroni Adjusted ANOVA-CNN with Transfer Learning for Lung X-Ray Image Classification

Abstract

A comprehensive approach for lung X-ray image classification is proposed, combining wavelet transform-based feature extraction with Bonferroni ANOVA feature selection and deep learning techniques. Features are initially extracted from lung X-ray images using wavelet transform, capturing crucial information across multiple scales. These features are concatenated to form a comprehensive feature set. To optimize this feature set, ANOVA is employed with Bonferroni adjusted feature selection to control the false positive rate and to identify the most relevant features, thereby reducing the dimensionality of the input data. This optimization can result in expedited training times and reduced computational resources required by the CNN. The selected features are subsequently fed into a Convolutional Neural Network (CNN) for classification. To further boost the CNN's performance, transfer learning is utilized by leveraging pre-trained models. Additionally, the performance of ANOVA-based feature selection is also compared with that of a genetic algorithm-based method. This integrated method, referred to as WTBAN-CNN-TL, aims to reduce the training times and resource requirements while improving the accuracy and efficiency of lung X-ray image classification.