Improvement of Gastroscopy Classification Performance Through Image Augmentation Using a Gradient-Weighted Class Activation Map
نویسندگان
چکیده
Endoscopic specialists performing gastroscopy, which relies on the naked eye, may benefit from a computer-aided diagnosis (CADx) system that employs deep learning. This report proposes utilizing CADx to classify normal and abnormal gastric cancer, gastritis, ulcer. The was trained using learning algorithm known as convolutional neural network (CNN). Specifically, Xception, includes depth-wise separable convolution, employed CNN. Image augmentation applied improve disadvantages of medical data, are difficult collect. A class activation map (CAM), an visualizes classified region interest in CNN, used cut paste image area into another image. CAM-identified lesion location augmented by pasting it divided nine equal parts pasted where variance difference minimal. Consequently, number images increased 360,905. Xception train dataset. confusion matrix evaluate performance gastroscopy system. criteria were specificity, sensitivity, F1 score, harmonic average precision, sensitivity (recall), AUC. score with original dataset 0.792 AUC 0.885. approach CAM presented this is shown be effective algorithm, improved 0.835, 0.903 terms respectively.
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ژورنال
عنوان ژورنال: IEEE Access
سال: 2022
ISSN: ['2169-3536']
DOI: https://doi.org/10.1109/access.2022.3207839