A Hybrid Attention-based Deep Model for Lung Cancer Subtype Classification from Multimodality Images

Abstract

Lung cancer is a deadly type of malignancy that poses a significant threat to human health. Accurately identifying the subtypes of lung cancer is critical for effective treatment. However, conventional methods for determining subtypes, such as histological examination, are invasive and time-consuming. In order to overcome this problem, a non-invasive approach for predicting lung cancer subtypes using multi-modality images with a hybrid model is proposed in this study. The model combines attention-based Convolutional Neural Networks (CNNs) and machine learning classifiers to achieve this objective. The model employs a soft-attention mechanism to focus on the pathological areas and extract both global and local features from the images. The stack-based ensemble classifier employs logistic regression as a meta-learner and four machine learning classifiers, including Support Vector Machine (SVM), Naive Bayes, Random Forest, and J48. The classifier categorizes lung cancer into Adenocarcinoma (ADC), Squamous Cell Carcinoma (SQC), and Small Cell Carcinoma (SCC). The suggested model validated using publically accessible datasets (Lung-PET-CT-DX, Lung1, and Lung3) achieved superior performance, with a validation accuracy of 98.8%, an F1-score of 0.986, and a Matthews Correlation Coefficient (MCC) of 0.988.