Most dominant metabolomic biomarkers identification for lung cancer

Ghosh, Utshab Kumar; Abir, Fuad Al; Rifaat, Nahian; Shovan, S.M.; Sayeed, Abu; Hasan, Md. Al Mehedi

doi:10.1016/j.imu.2021.100824

Cited by 7 publications

References 26 publications

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A comprehensive exploration of deep learning approaches for pulmonary nodule classification and segmentation in chest CT images

Canayaz,

Şehribanoğlu,

Özgökçe

et al. 2024

Neural Comput & Applic

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Accurately determining whether nodules on CT images of the lung are benign or malignant plays an important role in the early diagnosis and treatment of tumors. In this study, the classification and segmentation of benign and malignant nodules on CT images of the lung were performed using deep learning models. A new approach, C+EffxNet, is used for classification. With this approach, the features are extracted from CT images and then classified with different classifiers. In other phases of the study, a segmentation between benign and malignant was performed and, for the first time, a comparison of nodes was made during segmentation. The deep learning models InceptionV3, DenseNet121, and SeResNet101 were used as backbone models for feature extraction in the segmentation phase. In the classification phase, an accuracy of 0.9798, a precision of 0.9802, a recognition of 0.9798, an F1 score of 0.9798, and a kappa value of 0.9690 were achieved. During segmentation, the highest values of 0.8026 Jacard index and 0.8877 Dice coefficient were achieved.

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