Hyperspectral imaging: a new method for diagnosing benign and malignant lung cancer

Lung cancer is currently one of the malignant tumors that poses the biggest threat to health and life, as both its morbidity and death are increasing globally. The deep learning model has limited impact on the supplementary diagnostic accuracy of common medical samples when the morphological traits are unclear. More spectrum information may be found in the intracellular fluorescent fingerprint data from hyperspectral imaging, creating a novel sample type for tasks involving lung cancer categorization. This study examines the classification challenge of benign and malignant lung cancer using a variety of deep learning models. According to the experimental findings, hyperspectral fluorescence pictures may more clearly distinguish between benign and malignant lung cancer features. In one-dimensional data samples, convolutional neural networks perform better than random forests, but in two-dimensional data samples, they perform worse than residual network models. The 50-layer residual network model, with an accuracy of 0.98, has the greatest classification performance among the three deep residual network models. Hyperspectral fluorescence pictures have been proven to have improved outcomes in the detection of benign and malignant lung cancer through the research, which can better suit clinical needs and aid physicians in making clinical judgments.