A review on lung carcinoma segmentation and classification using CT image based on deep learning Online publication date: Fri, 16-Sep-2022
by S. Poonkodi; M. Kanchana
International Journal of Intelligent Systems Technologies and Applications (IJISTA), Vol. 20, No. 5, 2022
Abstract: Lung carcinoma was the first leading cause of death when compared to all other cancer. Early-stage detection of a lung nodule is an important step to prevent death and to increase the survival rate of patients with lung cancer. Various types of radiology techniques are used to acquire the image of lung nodules. Among the radiology technique, computed tomography (CT) is an effective method for diagnosing lung carcinoma at its early stage, thus reducing the mortality rate. Radiologists have faced a challenging task, that is, to calculate the accuracy of images due to the exponential growth of CT images. Nowadays, various computer vision techniques are available for the prediction and detection of carcinoma. Deep learning (DL) model provides a high level of services to the healthcare sector. DL techniques are becoming more efficient to detect and predict diseases at an early stage. This study reviews current work on the segmentation and classification of lung nodules in CT using DL models.
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