Deep Ensembling with Multimodal Image Fusion for Efficient Classification of Lung Cancer
This addresses the problem of efficient lung cancer diagnosis for medical imaging, though it appears incremental as it builds on existing ensemble and fusion methods.
This study tackled lung cancer classification from multimodal CT and PET images by developing a deep ensemble-based classifier with image fusion, which outperformed state-of-the-art networks on accuracy, F1-score, precision, and recall across three public datasets.
This study focuses on the classification of cancerous and healthy slices from multimodal lung images. The data used in the research comprises Computed Tomography (CT) and Positron Emission Tomography (PET) images. The proposed strategy achieves the fusion of PET and CT images by utilizing Principal Component Analysis (PCA) and an Autoencoder. Subsequently, a new ensemble-based classifier developed, Deep Ensembled Multimodal Fusion (DEMF), employing majority voting to classify the sample images under examination. Gradient-weighted Class Activation Mapping (Grad-CAM) employed to visualize the classification accuracy of cancer-affected images. Given the limited sample size, a random image augmentation strategy employed during the training phase. The DEMF network helps mitigate the challenges of scarce data in computer-aided medical image analysis. The proposed network compared with state-of-the-art networks across three publicly available datasets. The network outperforms others based on the metrics - Accuracy, F1-Score, Precision, and Recall. The investigation results highlight the effectiveness of the proposed network.