Cancer Diagnosis in Endoscopic Images using Discrete Wavelet Transform
محورهای موضوعی : Majlesi Journal of Telecommunication Devices
Sinan Ghanem
1
,
Mehran Emadi
2
1 - Master student in Computer Engineering, Isfahan (Khorasgan) branch, Islamic Azad University Isfahan, Iran
2 - signal processing
کلید واژه: Diagnosis, Destroyed tissues, Endoscopic Images, Based Stomach.,
چکیده مقاله :
Stomach cancer destroys the tissues of the digestive system. This cancer is one of the deadliest diseases. Endoscopic imaging is used to diagnose cancer. In endoscopy, the diagnosis of gastric cancer is difficult due to the similarity of the tissues, the low contrast of the image and the background. In order to overcome these problems, discrete wavelet transform has been used to detect stomach cancer. In the proposed method, there are data registration, data preprocessing, feature extraction, dimensionality reduction, and classification. The features are extracted with the help of discrete wavelet transform and then dimension reduction is done with the help of principal component analysis. The proposed approach was evaluated on datasets collected from five classes, including gastritis, ulcer, esophagitis , bleeding, and healthy. Jungle Tassafi has a value above 99% in all evaluation criteria, which represents the advantages of this category. The results of this research show that this method is accurate and reliable in diagnosis.
Stomach cancer destroys the tissues of the digestive system. This cancer is one of the deadliest diseases. Endoscopic imaging is used to diagnose cancer. In endoscopy, the diagnosis of gastric cancer is difficult due to the similarity of the tissues, the low contrast of the image and the background. In order to overcome these problems, discrete wavelet transform has been used to detect stomach cancer. In the proposed method, there are data registration, data preprocessing, feature extraction, dimensionality reduction, and classification. The features are extracted with the help of discrete wavelet transform and then dimension reduction is done with the help of principal component analysis. The proposed approach was evaluated on datasets collected from five classes, including gastritis, ulcer, esophagitis , bleeding, and healthy. Jungle Tassafi has a value above 99% in all evaluation criteria, which represents the advantages of this category. The results of this research show that this method is accurate and reliable in diagnosis.
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