Comparative Analysis of Deep Learning Methods on CT Images for Lung Cancer Specification

Lung cancer is the leading cause of cancer-related deaths worldwide, ranking first in men and second in women. Due to its aggressive nature, early detection and accurate localization of tumors are crucial for improving patient outcomes. This study aims to apply advanced deep learning techniques to i...

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Bibliographic Details
Published in:Cancers 2024-09, Vol.16 (19), p.3321
Main Authors: Kalkan, Muruvvet, Guzel, Mehmet S, Ekinci, Fatih, Akcapinar Sezer, Ebru, Asuroglu, Tunc
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Classification
Comparative analysis
Computed tomography
Datasets
Deep learning
Disease
Experiments
Literature reviews
Localization
Lung cancer
Lung diseases
Medical imaging
Medical research
Neural networks
Patients
Segmentation
Tomography
Tumors
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Summary:Lung cancer is the leading cause of cancer-related deaths worldwide, ranking first in men and second in women. Due to its aggressive nature, early detection and accurate localization of tumors are crucial for improving patient outcomes. This study aims to apply advanced deep learning techniques to identify lung cancer in its early stages using CT scan images. Pre-trained convolutional neural networks (CNNs), including MobileNetV2, ResNet152V2, InceptionResNetV2, Xception, VGG-19, and InceptionV3, were used for lung cancer detection. Once the disease was identified, the tumor's region was segmented using models such as UNet, SegNet, and InceptionUNet. The InceptionResNetV2 model achieved the highest detection accuracy of 98.5%, while UNet produced the best segmentation results, with a Jaccard index of 95.3%. The study demonstrates the effectiveness of deep learning models, particularly InceptionResNetV2 and UNet, in both detecting and segmenting lung cancer, showing significant potential for aiding early diagnosis and treatment. Future work could focus on refining these models and exploring their application in other medical domains.
ISSN:2072-6694
2072-6694
DOI:10.3390/cancers16193321