LW-MorphCNN: a lightweight morphological attention-based subtype classification network for lung cancer

Abstract

Abstract Lung cancer is generally considered one of the most deadly cancers globally. If it can be identified early and diagnosed correctly, the survival probability of patients can be significantly improved. In this process, histopathological examination is a commonly used method for diagnosing and detecting lung cancer. It is crucial to accurately identify lung cancer subtypes from histopathological images, as this helps doctors formulate effective treatment plans. However, the visual inspection in histopathological diagnosis requires a large amount of time and also depends on the subjective perception of clinicians. Therefore, this paper proposes a lightweight lung cancer subtype classification network based on morphological attention (LW-MorphCNN), which is used to automatically classify the histopathological images of benign lung tumors, ADC (adenocarcinoma), and SCC (squamous cell carcinoma) provided in the public dataset LC25000 (Lung and Colon). This paper takes histopathological images as input and conducts a comparative analysis with classic networks such as VGG16, VGG19, DenseNet121, and ResNet50, as well as existing classification methods proposed in the same work. The network proposed in this paper is superior to other networks in terms of parameter quantity and performance, with an accuracy rate and F1 - score reaching 99.47% and 99.44% respectively. Clinicians can install the provided LW-MorphCNN in the hospital to confirm the diagnosis results.