Lightweight Deep Learning Classification Model for Identifying Low-Resolution CT Images of Lung Cancer

Abstract

With an astounding five million fatal cases every year, lung cancer is among the leading causes of mortality worldwide for both men and women. The diagnosis of lung illnesses can benefit from the information a computed tomography (CT) scan can offer. The major goals of this study are to diagnose lung cancer and its seriousness and to identify malignant lung nodules from the provided input lung picture. This paper applies unique deep learning techniques to identify the exact location of the malignant lung nodules. Using a DenseNet model, mixed ground glass is analyzed in low-dose, low-resolution CT scan images of nodules (mGGNs) with a slice thickness of 5 mm in this study. This was done to categorize and identify many histological subtypes of lung cancer. Low-resolution CT scans are used to pathologically classify invasive adenocarcinoma (IAC) and minimally invasive adenocarcinoma (MIA). 105 low-resolution CT images with 5 mm thick slices from 105 patients at Lishui Central Hospital were selected. To detect and distinguish, IAC and MIA, extend and enhance deep learning two- and three-dimensional DenseNet models are used. The two-dimensional DenseNet model was shown to perform much better than the three-dimensional DenseNet model in terms of classification accuracy (76.67%), sensitivity (63.3%), specificity (100%), and area under the receiver operating characteristic curve (0.88). Finding the histological subtypes of persons with lung cancer should aid doctors in making a more precise diagnosis, even if the image quality is not outstanding.

Combined model integrating deep learning, radiomics, and clinical data to classify lung nodules at chest CT

“…Notably, GGNs are inherently challenging due to their blurred borders, potentially containing malignancies such as AIS. Liu et al achieved an 81.6% accuracy in a binary malignant versus benign classification in 204 patients [ 49 ], while Marappan et al achieved a 76.67% accuracy in distinguishing MIA from IA in a dataset of 105 patients [ 50 ]. Qi et al extended their study to 417 patients and classified nodules as small cell lung cancer (SCLC), IA, and SqCC.…”