CCSA-RF enhanced lightweight multi-scale CNN framework for robust lung cancer classification

Abstract

The early and accurate diagnosis of lung cancer is an urgent issue owing to the noise of imaging, nonhomogeneous nodule structures, and high cost of computing the current deep learning architectures. Most modern methods find it difficult to strike a balance between diagnostic accuracy, strength, and efficiency, especially with multimodal medical images. To overcome these shortcomings, this study proposes a single lightweight-based learning framework that combines preprocessing using wavelets, feature extraction under attention, chaos-based feature selection, and multiscale convolutional learning. First, a Ricker wavelet invariant center-weighted mean (RWICWM) filter was used to reduce noise without unimportant structures in the diagnostics. An Attention-Based DenseNet (ATT-DenseNet) is then used to extract discriminative features by highlighting the parts of the image that contain cancer-related features with channel-wise attention. The chaotic convolutional spectral analysis random forest (CCSA-RF) mechanism was proposed to select informative features and eliminate redundancy. Finally, a lightweight multi-scale CNN (LMS-CNN) is employed to achieve effective and precise classification of lung cancer. On the LC25000 and LIDC-IDRI datasets, the proposed framework attained an accuracy of 96.54, recall of 96.79, precision of 96.90, and F1-score of 96.32, with both datasets reporting similar results. The findings indicate that the proposed method enhances the classification accuracy and strength and still has a low computational complexity, which renders it applicable for real-world clinical use. The proposed research offers a computationally and theoretically sound solution that sets the state-of-the-art in lung cancer diagnostics.