TY - JOUR
T1 - Comparative Analysis of Deep Learning Methods on CT Images for Lung Cancer Specification
AU - Kalkan, Muruvvet
AU - Guzel, Mehmet S.
AU - Ekinci, Fatih
AU - Sezer, Ebru Akcapinar
AU - Asuroglu, Tunc
N1 - Publisher Copyright:
© 2024 by the authors.
PY - 2024/9/28
Y1 - 2024/9/28
N2 - Background: 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. Methods: 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. Results: 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%. Conclusions: 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.
AB - Background: 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. Methods: 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. Results: 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%. Conclusions: 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.
KW - classification
KW - CNN
KW - CT images
KW - deep learning
KW - lung cancer
KW - segmentation
UR - http://www.scopus.com/inward/record.url?scp=85206347433&partnerID=8YFLogxK
U2 - 10.3390/cancers16193321
DO - 10.3390/cancers16193321
M3 - Article
SN - 2072-6694
VL - 16
JO - Cancers
JF - Cancers
IS - 19
M1 - 3321
ER -