Automatic Lung Cancer Detection Using Artificial Intelligence
Session
Computer Science and Communication Engineering
Description
By far, lung cancer is the prominent cause of cancer deaths for both men and women around the world. In 2018, statistics for WHO (World Health Organization) showed that of 2.09 million people diagnosed with this disease, 1.76 million people have died. The survival rate increases if it is detected in its early stages. Taking into consideration the complexity of the problem, many computer-aided diagnosis systems that increase the survival rate have been proposed and developed. Driven by the notable success of deep learning in the area of complex image classification problems, this paper presents the use ResNet34, ResNet50, VGG16, and VGG19 convolutional neural network architectures or classifying images of patients with cancer. Moreover, to compare the performance evaluation Accuracy, Precision, Area Under Curve, and F1 score were calculated. In conclusion, ResNet50 architecture was estimated with the best result for this classification problem, with 87.04% Accuracy and 85.71% F1 score. The strategy of using pre-trained deep learning models proved to be pertinent to this problem.
Keywords:
Lung Cancer, Artificial Intelligence, Deep-Learning, Convolutional Neural Networks, ResNet50, Classification.
Session Chair
Bertan Karahoda
Session Co-Chair
Krenare Pireva
Proceedings Editor
Edmond Hajrizi
ISBN
978-9951-550-19-2
Location
Pristina, Kosovo
Start Date
26-10-2019 1:30 PM
End Date
26-10-2019 3:00 PM
DOI
10.33107/ubt-ic.2019.268
Recommended Citation
Bardh, Rushiti and Karahoda, Bertan, "Automatic Lung Cancer Detection Using Artificial Intelligence" (2019). UBT International Conference. 268.
https://knowledgecenter.ubt-uni.net/conference/2019/events/268
Automatic Lung Cancer Detection Using Artificial Intelligence
Pristina, Kosovo
By far, lung cancer is the prominent cause of cancer deaths for both men and women around the world. In 2018, statistics for WHO (World Health Organization) showed that of 2.09 million people diagnosed with this disease, 1.76 million people have died. The survival rate increases if it is detected in its early stages. Taking into consideration the complexity of the problem, many computer-aided diagnosis systems that increase the survival rate have been proposed and developed. Driven by the notable success of deep learning in the area of complex image classification problems, this paper presents the use ResNet34, ResNet50, VGG16, and VGG19 convolutional neural network architectures or classifying images of patients with cancer. Moreover, to compare the performance evaluation Accuracy, Precision, Area Under Curve, and F1 score were calculated. In conclusion, ResNet50 architecture was estimated with the best result for this classification problem, with 87.04% Accuracy and 85.71% F1 score. The strategy of using pre-trained deep learning models proved to be pertinent to this problem.