.png){kind=icon}
.png){kind=icon}
Return
Optimizing a Deep Learning System for Lung Cancer Classification
.png){kind=icon}
PRE
AI
DOI：10.1007/978-3-032-10731-2_26.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Lung cancer is one of the leading causes of mortality worldwide. Early and accurate detection of lung lesions is essential to improve clinical outcomes. In this study, a convolutional neural network (CNN) model was developed to classify lung computed tomography (CT) images. The current study processed the dataset IQOTH/NCCD_lung_cancer_dataset. A strategy that includes data augmentation techniques was designed to optimize the generalization of the model. When evaluating the model on the test set, an accuracy of 0.9819, a precision of 0.9575, a recall of 0.9742, and an F1-Score of 0.9653 were obtained, indicating outstanding performance in image classification. These metrics were derived from the confusion matrix, which demonstrated minimal misclassification, particularly between benign and normal categories, while achieving perfect identification of malignant cases. These results suggest that the proposed model is a promising tool for supporting the diagnosis of lung lesions in CT images.
Keywords:
lung cancer
computed tomography
medical image analysis
image classification
data augmentation
deep learning
CNN
ResNet
Journal
P
IF:
0
Papers:
160
Citations:
0