# Lung Cancer Detection Model Using Deep Learning Technique
###### Abstract  
Globally, lung cancer (LC) is the primary factor for the highest cancer-related mortality rate. Deep learning (DL)-based medical image analysis plays a crucial role in LC detection and diagnosis. It can identify early signs of LC using positron emission tomography (PET) and computed tomography (CT) images. However, the existing DL-based LC detection models demand substantial computational resources. Healthcare centers face challenges in handling the complexities in the model implementation. Therefore, the author aimed to build a DL-based LC detection model using PET/CT images. Effective image preprocessing and augmentation techniques were followed to overcome the noises and artifacts. A convolutional neural network (CNN) model was constructed using the DenseNet-121 model for feature extraction. The author applied deep autoencoders to minimize the feature dimensionality. The MobileNet V3-Small model was used to identify the types of LC using the features. The author applied quantization-aware training and early stopping strategies to improve the proposed LC detection accuracy with less computational power. In addition, the Adam optimization (AO) algorithm was used to fine-tune the hyper-parameters in order to reduce the training time for detecting the LC type. The Lung-PET-CT-Dx dataset was used for performance evaluation. The experimental outcome highlighted that the proposed model obtained an accuracy of 98.6 and a Cohen's Kappa value of 95.8 with fewer parameters. The proposed model can be implemented in real-time to support radiologists and physicians in detecting LC in the earlier stages. In the future, liquid neural networks and ensemble learning techniques will be used to enhance the performance of the proposed LC detection model. lung cancer; machine learning; deep learning; positron emission tomography; computed tomography; convolutional neural network +
Footnote †: journal: Medical  
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the suggested LC detection model. The proposed model produced an outstanding result for each class using the PET/CT images. It is evident that the suggested image preprocessing and augmentation techniques assisted the proposed LC detection model in identifying the LC types. Table 7 emphasized the key findings of the comparison analysis. The suggested model surpassed the existing LC detection approaches by reaching an optimal accuracy and F1-score using minimal resource utilization. Finally, Table 8 showed that the proposed model obtained a higher AU-ROC and AU-PRC with a minimum loss of 0.89. +++  
[PERSON] and [PERSON] [37] developed an image classification model for detecting LC from PET/CT images. They obtained an optimal outcome using the Lung-PET-CT-Dx dataset. Likewise, the proposed model achieved superior results by employing effective image processing, feature extraction, and image classification models. The DenseNet-121-based LC detection model achieved an accuracy of 96.4 with parameters of 8.3 and FLOPs of 679. However, the proposed model obtained exceptional accuracy with limited computational resources. The EfficientNet-based LC detection model obtained a higher accuracy and F1-score. However, the model may not perform optimally on the real-time images. It required a considerable computational resource for classifying the PET/CT images. In addition, it demands additional training time to capture the fine details of the images. The ShuffleNet-based LC detection model offered an exceptional outcome. However, the shallower architecture of the ShuffleNet model may limit the LC detection performance. In contrast, the proposed LC detection model presented an outstanding result with limited computational resources. The combination of the DenseNet-121 and MobileNet V3 models yielded better results. PET/CT imaging offers a complete assessment of lung tissue, effectively highlighting regions exhibiting elevated metabolic activity while offering valuable anatomical context.