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. 2023 Jun 2:13:1193746.
doi: 10.3389/fonc.2023.1193746. eCollection 2023.

A multi-class deep learning model for early lung cancer and chronic kidney disease detection using computed tomography images

Ananya Bhattacharjee  1 , Sameh Rabea  2 , Abhishek Bhattacharjee  3 , Eslam B Elkaeed  2 , R Murugan  1 , Heba Mohammed Refat M Selim  2   4 , Ram Kumar Sahu  5 , Gamal A Shazly  6 , Mounir M Salem Bekhit  6
Affiliations

A multi-class deep learning model for early lung cancer and chronic kidney disease detection using computed tomography images

Ananya Bhattacharjee et al. Front Oncol. .

Abstract

Lung cancer is a fatal disease caused by an abnormal proliferation of cells in the lungs. Similarly, chronic kidney disorders affect people worldwide and can lead to renal failure and impaired kidney function. Cyst development, kidney stones, and tumors are frequent diseases impairing kidney function. Since these conditions are generally asymptomatic, early, and accurate identification of lung cancer and renal conditions is necessary to prevent serious complications. Artificial Intelligence plays a vital role in the early detection of lethal diseases. In this paper, we proposed a modified Xception deep neural network-based computer-aided diagnosis model, consisting of transfer learning based image net weights of Xception model and a fine-tuned network for automatic lung and kidney computed tomography multi-class image classification. The proposed model obtained 99.39% accuracy, 99.33% precision, 98% recall, and 98.67% F1-score for lung cancer multi-class classification. Whereas, it attained 100% accuracy, F1 score, recall and precision for kidney disease multi-class classification. Also, the proposed modified Xception model outperformed the original Xception model and the existing methods. Hence, it can serve as a support tool to the radiologists and nephrologists for early detection of lung cancer and chronic kidney disease, respectively.

Keywords: artificial intelligence; computed tomography; fine-tuning; kidney diseases; lung cancer; modified Xception model; transfer learning.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
IQ-OTHNCCD dataset (A) Benign (B) Malignant (C) Normal.
Figure 2
Figure 2
CT Kidney dataset (A) Normal (B) Cyst (C) Tumor (D) Stone.
Figure 3
Figure 3
The proposed modified XceptionNet model.
Figure 4
Figure 4
Train Acc, Validation Acc, Training loss, Validation loss curves of NCTS dataset (A) Inception ResNet V2 (B) Inception V3 (C) NASNet Large (D) Proposed model.
Figure 5
Figure 5
Confusion matrix of NCTS (A) Inception resnetv2 (B) Inception v3 (C) NASNet Large (D) Proposed model.
Figure 6
Figure 6
Train Acc, Validation Acc, Train loss, Validation loss curves of lung cancer MNoB dataset (A) Inception ResNet V2 (B) Inception V3 (C) MobileNet V3 Small (D) Proposed model.
Figure 7
Figure 7
Confusion matrix of MNoB (A) Inception resnetv2 (B) Inception v3 (C) MobileNet V3 Small (D) Proposed model.
Figure 8
Figure 8
Computational time comparison of the original Xception model and the proposed Xception model in minutes.
Figure 9
Figure 9
ROC curves (A) NCTS (B) MNoB.
See this image and copyright information in PMC

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