The automatic diagnosis of lung cancer using chest X-ray (CXR) images has significantly advanced with progress in computing, machine learning, and deep learning. However, detecting lesions and nodules remains challenging due to CXR limitations. Early lung cancer detection is critical for successful treatment, but current AI algorithms often rely on large annotated datasets, which are not always available. To address this, a novel multi-classification deep learning framework is proposed that combines CXR and CT images. This approach leverages the detailed feature detection capabilities of CT scans alongside the complementary views from CXRs, improving early-stage lung cancer detection and classification precision. The proposed framework integrates the Binary Adaptive Crossover Particle Whale Optimization Algorithm-S (BACP-WOA-S) to efficiently address high-dimensionality challenges. The proposed framework includes key contributions: Employing a Binary Whale Optimization Algorithm variation (BWOA) is used in the feature selection (FS) layer to reduce computational complexity while preserving key features. Additionally, a Feed-Forward Neural Network (FFNN) is utilized in the Deep Learning (DL) layer to optimize characteristics like layers, neurons, and activation functions. The framework, powered by H2O for scalable deep learning processes, achieves a 97.6% accuracy rate, showcasing its effectiveness in early lung cancer detection.
Ibrahim et al. (Sat,) studied this question.