Deep fusion of gray level co-occurrence matrices for lung nodule classification

Abstract

Lung cancer is a serious threat to human health, with millions dying because of its late diagnosis. The computerized tomography (CT) scan of the chest is an efficient method for early detection and classification of lung nodules. The requirement for high accuracy in analyzing CT scan images is a significant challenge in detecting and classifying lung cancer. In this paper, a new deep fusion structure based on the long short-term memory (LSTM) has been introduced, which is applied to the texture features computed from lung nodules through new volumetric grey-level-co-occurrence-matrices (GLCMs), classifying the nodules into benign, malignant, and ambiguous. Also, an improved Otsu segmentation method combined with the water strider optimization algorithm (WSA) is proposed to detect the lung nodules. WSA-Otsu thresholding can overcome the fixed thresholds and time requirement restrictions in previous thresholding methods. Extended experiments are used to assess this fusion structure by considering 2D-GLCM based on 2D-slices and approximating the proposed 3D-GLCM computations based on volumetric 2.5D-GLCMs. The proposed methods are trained and assessed through the LIDC-IDRI dataset. The accuracy, sensitivity, and specificity obtained for 2D-GLCM fusion are 94.4%, 91.6%, and 95.8%, respectively. For 2.5D-GLCM fusion, the accuracy, sensitivity, and specificity are 97.33%, 96%, and 98%, respectively. For 3D-GLCM, the accuracy, sensitivity, and specificity of the proposed fusion structure reached 98.7%, 98%, and 99%, respectively, outperforming most state-of-the-art counterparts. The results and analysis also indicate that the WSA-Otsu method requires a shorter execution time and yields a more accurate thresholding process.

Dynamic learning for imbalanced data in learning chest X-ray and CT images

“…Several studies have proposed the use of deep learning models for the detection of lung cancer and other lung diseases. These models often focus on the detection of thorax diseases using approaches such as 3D Convolutional Neural Networks (CNNs) and multi-scale prediction strategies [38] , [39] , [40] , [41] , [42] , [43] .…”

Gray Level Co-Occurrence Matrix and RVFL for Covid-19 Diagnosis

“…After calculating the co-occurrence matrix, it is often not to apply directly but to calculate the texture feature quantity on this basis. Researchers often use contrast, energy, entropy, correlation, and other feature quantity to express the texture feature [31].…”

Esophageal cancer detection via non-contrast CT and deep learning

“…In radiology, DL techniques have the most significant impact: lesion or disease detection ( 13–15 ), classification ( 16 , 17 ), quantification, and segmentation ( 12 , 17 , 18 ). Examples of these applications include the identification of pulmonary nodules ( 19 , 20 ) and breast cancer ( 21 ), classification of benign or malignant lung nodules ( 22 ) and breast tumors ( 23 ), utilization of texture-based radiomic features for predicting therapy response in gastrointestinal cancer ( 24 ), and segmentation of brain anatomy ( 25 , 26 ).…”