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. 2023 Jun 23;15(13):3300.
doi: 10.3390/cancers15133300.

Al-Biruni Earth Radius Optimization with Transfer Learning Based Histopathological Image Analysis for Lung and Colon Cancer Detection

Rayed AlGhamdi  1 , Turky Omar Asar  2 , Fatmah Y Assiri  3 , Rasha A Mansouri  4   5 , Mahmoud Ragab  1   6   7
Affiliations

Al-Biruni Earth Radius Optimization with Transfer Learning Based Histopathological Image Analysis for Lung and Colon Cancer Detection

Rayed AlGhamdi et al. Cancers (Basel). .

Abstract

An early diagnosis of lung and colon cancer (LCC) is critical for improved patient outcomes and effective treatment. Histopathological image (HSI) analysis has emerged as a robust tool for cancer diagnosis. HSI analysis for a LCC diagnosis includes the analysis and examination of tissue samples attained from the LCC to recognize lesions or cancerous cells. It has a significant role in the staging and diagnosis of this tumor, which aids in the prognosis and treatment planning, but a manual analysis of the image is subject to human error and is also time-consuming. Therefore, a computer-aided approach is needed for the detection of LCC using HSI. Transfer learning (TL) leverages pretrained deep learning (DL) algorithms that have been trained on a larger dataset for extracting related features from the HIS, which are then used for training a classifier for a tumor diagnosis. This manuscript offers the design of the Al-Biruni Earth Radius Optimization with Transfer Learning-based Histopathological Image Analysis for Lung and Colon Cancer Detection (BERTL-HIALCCD) technique. The purpose of the study is to detect LCC effectually in histopathological images. To execute this, the BERTL-HIALCCD method follows the concepts of computer vision (CV) and transfer learning for accurate LCC detection. When using the BERTL-HIALCCD technique, an improved ShuffleNet model is applied for the feature extraction process, and its hyperparameters are chosen by the BER system. For the effectual recognition of LCC, a deep convolutional recurrent neural network (DCRNN) model is applied. Finally, the coati optimization algorithm (COA) is exploited for the parameter choice of the DCRNN approach. For examining the efficacy of the BERTL-HIALCCD technique, a comprehensive group of experiments was conducted on a large dataset of histopathological images. The experimental outcomes demonstrate that the combination of AER and COA algorithms attain an improved performance in cancer detection over the compared models.

Keywords: computer-aided diagnosis; lung and colon cancer; medical image analysis; parameter optimization; transfer learning.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Overall flow of the BERTL-HIALCCD approach.
Figure 2
Figure 2
Framework of the CRNN.
Figure 3
Figure 3
Sample images. (a) Lung cancer (b) Colon cancer.
Figure 4
Figure 4
Confusion matrices of the BERTL-HIALCC system: (a,b) 80:20 of TRP/TSP and (c,d) 70:30 of TRP/TSP.
Figure 5
Figure 5
LCC recognition outcomes of the BERTL-HIALCC system on 70% of TRP.
Figure 6
Figure 6
LCC recognition outcomes of the BERTL-HIALCC system on 30% of TSP.
Figure 7
Figure 7
LCC recognition outcomes of the BERTL-HIALCC system on 80% of TRP.
Figure 8
Figure 8
LCC recognition outcomes of the BERTL-HIALCC system on 30% of TSP.
Figure 9
Figure 9
Accuracy curve of the BERTL-HIALCC system on 80:20 of TRP/TSP.
Figure 10
Figure 10
Loss curve of the BERTL-HIALCC system on 80:20 of TRP/TSP.
Figure 11
Figure 11
PR curve of the BERTL-HIALCC system on 80:20 of TRP/TSP.
Figure 12
Figure 12
ROC curve of the BERTL-HIALCC system on 80:20 of TRP/TSP.
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