International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 09 Issue: 07 | July 2022
p-ISSN: 2395-0072
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Shap Analysis Based Gastric Cancer Detection Varanasi L V S K B Kasyap1, Athuluri Sai Kushal1, Sunkara Vinisha1 1School
of Computer Science and Engineering, VIT-AP University, Inavolu, India. ---------------------------------------------------------------------***--------------------------------------------------------------------steps, and stages. There are several transitional phases that Abstract - Gastric Cancer is one of the most widely
includes the precancerous state namely "Normal gastric mucosa - chronic non-atrophic gastritis - atrophic gastritis intestinal metaplasia - dysplasia - gastric cancer," as per the observations of Correa's current more widely accepted pattern of human GC [4]. Atrophic gastritis (AG) and intestinal metaplasia (IM) are two conditions that are thought to be precancerous lesions that are strongly linked to GC [5]. If not treated in a timely manner, AG and IM have a higher chance of turning into GC. For the prevention and treatment of GC, their early detection and prompt treatment have significant practical implications.
reported problem in the world causing high modality rates in the recent times. Gastroscopy is one of the efficient methods that is widely used to analyze the gastric problems. The advent technology of deep learning helps the doctor to assist in detecting the gastric cancer in the early stages. The performance of the existing methods in detecting gastric cancer from the images are not so high and consuming longer times. This paper proposes a novel deep learning frame work that can be used to detect gastric cancer from the gastric slice images. The proposed methos is based on patch-based analysis of the given input image. Specifically, the model selects and extracts the features from the images in training phase and evaluates the true risk of the patients. This is one of the novel contributions of the proposed work. The Bag-of-features techniques will be applied on the extracted features in the proposed network for the selected patches for better analysis. Experimental results prove that the proposed framework is able to detect the gastric cancer from the images effectively and efficiently. The model is robust enough to detect the minute lesions that can cause the gastric tumor in the further stages. The dataset used in this analysis is publicly available and the results achieved by this model are higher than the other models that use the same dataset. The proposed framework is also compared with existing frameworks, giving the accuracy scores higher for the proposed model.
Examination of GC could be done with the help of various sources namely imaging tests, pathological images and endoscopy. To initiate with, stomach cancer has to be detected successfully via endoscopy. The surface structure can be precisely analysed by image-enhanced endoscopic techniques including narrow-band imaging [6] and linked colour imaging [7]. According to the studies, the precision of gastrointestinal tumour diagnosis [8] could be augmented by the deployment of endoscopic techniques. However, there is a research which has stated that even endoscopy examinations lead to still missed 10% of upper gastrointestinal malignancies [9]. There would be missed diagnoses in an endoscopic unit even if two experts participated [10]. The cause was that accurate gastroscopy image diagnosis requires years of practise to develop. Second, the gold standard for tumour diagnosis is histological image recognition. Diagnostic mistakes and a heavy workload for pathologists have been brought on by the dearth of pathologists [11]. Last but not least, imaging tests are crucial in assessing the lymph node metastases of stomach cancer. The primary focus of an imaging evaluation is on the morphological characteristics of the lesions. For instance, the perigastric adipose tissue is so dense that it resembles lymph nodes. Doctors may make errors in diagnosis due to inexperience and missing diagnoses. The accuracy of the diagnosis will eventually decline, particularly when there are several cases [12].
Key Words: Gastric Cancer, Stomach neoplasms, Endoscopy, Convolutional Neural Network, Deep Learning
1.INTRODUCTION This Stomach cancer, often known as Gastric Cancer(GC), is a type of cancer. When cells in the stomach's lining grow out of control, they develop into tumours that can infiltrate healthy tissues and spread to other regions of the body. Global data show that GC is the second most common cause of cancerrelated fatalities and the fourth most prevalent malignancy worldwide [1]. Environmental and genetic factors, among others, play a complex role in the onset and development of GC, and their effects on these processes have not yet been fully understood. Even after receiving a full course of treatment that includes surgery, chemotherapy, and radiotherapy, the five-year survival percentage for advanced GC is still less than 30% [2], whereas the five-year survival rate for early GC can be over 90%, sometimes even having a curative impact [3]. The incidence and development of GC is a complicated process involving numerous mechanisms,
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