A multichannel analysis of imbalanced computed tomography data for lung cancer classification

Abstract

Lung cancer holds the highest fatality rate among cancers, emphasizing the importance of early detection. Computer algorithms have gained prominence across various domains, including lung cancer diagnosis. These algorithms assist specialists, especially in medical imaging, yet current efforts lack comprehensive CT data analysis; especially in handling imbalanced datasets and fully exploiting spatial information. The lack of spatial analysis hinders the ability to identify subtle variations in texture and structure that are crucial for detecting lung cancer early and accurately. Therefore, this study uses a multichannel analysis of computed tomography (CT) images and deep learning-based ensemble learning (MC-ECNN) to find lung cancer even when the data is not balanced. Firstly, the data imbalance issue is tackled through the synthetic minority oversampling technique (SOMTE); afterwards, a multi-channel analysis of the data is performed to explore a distinct set of abstract features. Lastly, a deep ensemble learning method is used to classify the extracted distinct abstract feature set into the appropriate classes. The proposed method uses the discrete Fast Fourier transform (DFFT) and discrete cosine transform (DCT), along with the actual CT scans, for the multi-channel analysis of the data in different domains. The proposed model yielded 99.60% test accuracy on unseen data, which is at least 3% better than the other state-of-the-art studies considered for the comparison. In addition to the classification accuracy, the efficacy of the proposed model has also been justified through precision, recall, F1-score, support value, and misclassification rate.