Original scientific paper
https://doi.org/10.1080/00051144.2024.2304367
Automated identification of gastric cancer in endoscopic images by a deep learning model
C. Jasphin
; Department of CSE, Arunachala College of Engineering for Women, Vellichanthai, India
*
J. Merry Geisa
; Department of EEE, St. Xavier’s Catholic College of Engineering, Nagercoil, India
* Corresponding author.
Abstract
Gastric cancer is a deadly disease which should be treated in time, in order to increase the life
span of the patient. Computer aided diagnosis will help the doctors to identify the gastric cancer
easily. In this paper, a CAD based approach is projected to discriminate and categorize gastric
cancers from various other intestinal disorders. The approach provided the Xception network,
with individual convolutions. The projected technique applied three procedures: Google’s Auto
Augment for augmentation purpose, BCGDU-Net for segmentation and Xception network for
lesion classification. The augmentation and segmentation facilitated theclassifying technique to
be enhanced because this methodology prohibited overfitting. The segmented region is classified as cancerous or non-cancerous based on the features extracted in the Xception network
training phase. This method is analyzed with the different combinations of augmentation, segmentation with and without ROC. It is found that the area under ROC curve for augmentation
and segmentation is higher than the other two cases. Moreover, this technique provides a segmentation accuracy of 98% when compared with existing methods like fuzzy C means, global
thresholding, BCD-Net, U Net. The classification accuracy of 98.9% is obtained, which is higher
than the existing techniques like Res Net, VGG net, Mobile Net.
Keywords
BCGDU-Net; Xception network; Google’s auto augment LSTM; GRU; CNN; data augmentation
Hrčak ID:
323047
URI
Publication date:
7.2.2024.
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