Diagnosis of Gastric Cancer via Classification of the Tongue Images using Deep Convolutional Networks
الموضوعات :Elham Gholami 1 , Seyed Reza Kamel Tabbakh 2 , Maryam khairabadi 3
1 - Department of Computer Engineering, Neyshabur branch, Islamic Azad University, Neyshabur,Iran
2 - Department of Computer Engineering, Mashhad Branch,Islamic Azad University, Mashhad, Iran
3 - Department of Computer Engineering, Neyshabur Branch,Islamic Azad University, Neyshabur,Iran
الکلمات المفتاحية: Gastric Cancer, Deep Convolutional Networks, Image Classification, Fine-grained Recognition,
ملخص المقالة :
Gastric cancer is the second most common cancer worldwide, responsible for the death of many people in society. One of the issues regarding this disease is the absence of early and accurate detection. In the medical industry, gastric cancer is diagnosed by conducting numerous tests and imagings, which are costly and time-consuming. Therefore, doctors are seeking a cost-effective and time-efficient alternative. One of the medical solutions is Chinese medicine and diagnosis by observing changes of the tongue. Detecting the disease using tongue appearance and color of various sections of the tongue is one of the key components of traditional Chinese medicine. In this study, a method is presented which can carry out the localization of tongue surface regardless of the different poses of people in images. In fact, if the localization of face components, especially the mouth, is done correctly, the components leading to the biggest distinction in the dataset can be used which is favorable in terms of time and space complexity. Also, since we have the best estimation, the best features can be extracted relative to those components and the best possible accuracy can be achieved in this situation. The extraction of appropriate features in this study is done using deep convolutional neural networks. Finally, we use the random forest algorithm to train the proposed model and evaluate the criteria. Experimental results show that the average classification accuracy has reached approximately 73.78 which demonstrates the superiority of the proposed method compared to other methods.
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