التفاصيل البيبلوغرافية
| العنوان: |
Histopathological Lung Cancer Detection Using Enhanced Grasshopper Optimization Algorithm with Random Forest. |
| المؤلفون: |
Pradhan, Manaswini, Bhuiyan, Alauddin, Mishra, Subhankar, Thieu, Thanh, Coman, Ioana L. |
| المصدر: |
International Journal of Intelligent Engineering & Systems; 2022, Vol. 15 Issue 6, p11-20, 10p |
| مصطلحات موضوعية: |
LUNG cancer, RANDOM forest algorithms, MATHEMATICAL optimization, LUNGS, GRASSHOPPERS, HISTOPATHOLOGY, FEATURE extraction |
| مستخلص: |
In the recent decades, the lung cancer is the most dangerous disease that causes 1.6 million deaths per year. Therefore, the early recognition of lung cancer is an effective way to reduce the mortality rate. Compared to other imaging techniques, the histopathology images are effective in identifying the location and level of cancer. In this manuscript, a novel model is implemented for an automatic classification of histopathological images related to lung tissues. Initially, the color normalization technique is applied for improving the contrast of the histopathological images, which are acquired from the LC25000 lung histopathological image dataset. Additionally, the cancer segmentation is accomplished utilizing saliency driven region edge based top down level set (SDREL). Further, the feature descriptors: Alexnet and gray level co-occurrence matrix (GLCM) features were used for extracting the feature vectors from the segmented histopathology images. Lastly, the enhanced grasshopper optimization algorithm (EGOA) and random forest classifier were used for optimal feature selection and lung tissue classification. The simulation result shows that the EGOA-random forest model obtained 98.50 % of accuracy on the LC25000 lung histopathological image dataset. [ABSTRACT FROM AUTHOR] |
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| قاعدة البيانات: |
Complementary Index |
