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. 2023 Aug 5;15(15):3982.
doi: 10.3390/cancers15153982.

Dung Beetle Optimization with Deep Feature Fusion Model for Lung Cancer Detection and Classification

Mohammad Alamgeer  1 , Nuha Alruwais  2 , Haya Mesfer Alshahrani  3 , Abdullah Mohamed  4 , Mohammed Assiri  5
Affiliations

Dung Beetle Optimization with Deep Feature Fusion Model for Lung Cancer Detection and Classification

Mohammad Alamgeer et al. Cancers (Basel). .

Abstract

Lung cancer is the main cause of cancer deaths all over the world. An important reason for these deaths was late analysis and worse prediction. With the accelerated improvement of deep learning (DL) approaches, DL can be effectively and widely executed for several real-world applications in healthcare systems, like medical image interpretation and disease analysis. Medical imaging devices can be vital in primary-stage lung tumor analysis and the observation of lung tumors from the treatment. Many medical imaging modalities like computed tomography (CT), chest X-ray (CXR), molecular imaging, magnetic resonance imaging (MRI), and positron emission tomography (PET) systems are widely analyzed for lung cancer detection. This article presents a new dung beetle optimization modified deep feature fusion model for lung cancer detection and classification (DBOMDFF-LCC) technique. The presented DBOMDFF-LCC technique mainly depends upon the feature fusion and hyperparameter tuning process. To accomplish this, the DBOMDFF-LCC technique uses a feature fusion process comprising three DL models, namely residual network (ResNet), densely connected network (DenseNet), and Inception-ResNet-v2. Furthermore, the DBO approach was employed for the optimum hyperparameter selection of three DL approaches. For lung cancer detection purposes, the DBOMDFF-LCC system utilizes a long short-term memory (LSTM) approach. The simulation result analysis of the DBOMDFF-LCC technique of the medical dataset is investigated using different evaluation metrics. The extensive comparative results highlighted the betterment of the DBOMDFF-LCC technique of lung cancer classification.

Keywords: computer-aided diagnosis; deep learning; dung beetle optimizer; feature fusion model; lung cancer.

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Conflict of interest statement

The authors declare that they have no conflict of interest. The manuscript was written through the contributions of all authors. All authors have approved the final version of the manuscript.

Figures

Figure 1
Figure 1
Overall flow of DBOMDFF-LCC system.
Figure 2
Figure 2
Sample images. (a) Normal, (b) benign, (c) malignant [32].
Figure 3
Figure 3
Confusion matrices of DBOMDFF-LCC system. (a,b) 80:20 of TRP/TSP and (c,d) 70:30 of TRP/TSP.
Figure 4
Figure 4
Average outcome of DBOMDFF-LCC approach on 80:20 of TRP/TSP.
Figure 5
Figure 5
Average outcome of DBOMDFF-LCC system on 70:30 of TRP/TSP.
Figure 6
Figure 6
(a,c) Accuracy curve on 80:20/70:30 and (b,d) loss curve on 80:20/70:30.
Figure 7
Figure 7
(a,c) PR curve on 80:20/70:30 and (b,d) ROC curve on 80:20/70:30.
Figure 8
Figure 8
Comparative outcome of DBOMDFF-LCC system with other approaches.
See this image and copyright information in PMC

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