A Novel Approach for Increased Convolutional Neural Network Performance in Gastric-Cancer Classification Using Endoscopic Images

• Published in: IEEE access Vol. 9; pp. 51847 - 51854
• Main Authors: Lee, Sin-Ae, Cho, Hyun Chin, Cho, Hyun-Chong
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Artificial neural networks; Augmentation; Biopsy; Bleeding; Cancer; Clustering; computer-aided diagnosis (CADx); deep learning; Endoscopes; Gastric cancer; Ground truth; Image classification; Image color analysis; Image segmentation; Internet; Iterative methods; Lesions; Medical imaging; Performance measurement; segmentation; Training; Ulcers
• DOI: 10.1109/ACCESS.2021.3069747
• ISSN: 2169-3536

Gastric cancer is the third-most-common cause of cancer-related deaths in the world. Fortunately, it can be detected using endoscopy equipment. Computer-aided diagnosis (CADx) systems can help clinicians identify cancer from gastric diseases more accurately. In this paper, we present a CADx system that distinguishes and classifies gastric cancer from pre-cancerous conditions, such as gastric polyps, gastric ulcers, gastritis, and bleeding. The system uses a deep-learning model, Xception, which involves depth-wise separable convolutions, to classify cancer and non-cancers. The proposed method consists of two steps: Google's AutoAugment for augmentation and the simple linear iterative clustering (SLIC) superpixel and fast and robust fuzzy C-means (FRFCM) algorithm for image segmentation during preprocessing. These approaches produce a feasible method of distinguishing and classifying cancers from other gastric diseases. Based on biopsy-supported ground truth, the performance metrics of the area under the receiver operating characteristic curve (i.e. Az) are measured on the test sets. Based on the classification results, the Az of the proposed classification model is 0.96, which is 0.06 up from 0.90 which is the Az of the original data. Our methods are fully automated without the manual specification of region-of-interests for the test and with a random selection of images for model training. This methodology may play a crucial role in selecting effective treatment options without the need for a surgical biopsy.