Computer-Aided Gastrointestinal Diseases Analysis From Wireless Capsule Endoscopy: A Framework of Best Features Selection

Abstract

The continuous improvements in the area of medical imaging, makes the patient monitoring a crucial concern. The internet of things (IoT) embedded in a medical technologies to collect data from human body through sensors, wireless connectivity etc. The junction of medicine and IT like medical informatics will transform healthcare, curbing cost, make more efficient, and saving lives. Various computerized techniques are implemented in the area of Artificial Intelligence (AI) for the application of medical imaging to diagnose the infected regions in the images and videos such as WCE and pathology. The famous stomach infections are ulcer, polyp, and bleeding. Stomach cancer is the most common infection and a leading cause of human deaths worldwide. In the USA, since 2019, a total of 27,510 new cases are reported including 17,230 men and 10,230 women. While the number of deaths is 11,140 consists of 6,800 men and 4,340 women. The manual diagnosis of these stomach infections is a difficult and agitated process therefore it is required to design a fully automated system using AI. In this article, we presented a fully automated system for stomach infection recognition based on deep learning features fusion and selection. In this design, ulcer images are assigned manually and support to a saliency-based method for ulcer detection. Later, pre-trained deep learning model named VGG16 is employing and retrained using transfer learning. Features of re-trained model are extracted from two consecutive fully connected layers and fused by array-based approach. Besides, the best individuals are selected through the metaheuristic approach name PSO along mean value-based fitness function. The selected individuals are finally recognized through Cubic SVM. The experiments are conducted on Private collected dataset and achieved an accuracy of 98.4%, which is best as compared to existing state-of-the-art techniques.

GASTRO-CADx: a three stages framework for diagnosing gastrointestinal diseases

“…Therefore, it is commonly used with the huge dimension of DL features extracted from CNNs Jadoon et al, 2017;Zhang et al, 2020;Das et al, 2020;Xue et al, 2016;Leng et al, 2016;Wu et al, 2018;Sampaio et al, 2011) achieving outperforming results. Also, as you can see in Table 1, that SVM is the commonly used in the literature It can be observed that articles that used SVM achieved the highest performance as Khan et al (2020b) which achieved an accuracy of 99.13%, (Khan et al, 2020a) achieving an accuracy of 98.4%, obtaining an accuracy of 95%, (Billah, Waheed & Rahman, 2017) achieving an accuracy of 98.65%…”

“…Feature extraction methods based on DL techniques have been extensively utilized in the literature Kim, Cho & Cho, 2019;Lee et al, 2019). The authors of Khan et al (2020a) proposed a CADx system to detect ulcers and bleeding GI diseases. Their system extracted deep features from two different layers of VGG-16 CNN.…”

From ECG signals to images: a transformation based approach for deep learning

“…Recently, deep learning shows huge success in the medical domain (Anjum et al, 2020). It is used for many imaging (Khan et al, 2020a(Khan et al, , 2020bFernandes, Rajinikanth & Kadry, 2019), disease recognition (Sahlol et al, 2020a(Sahlol et al, , 2020bCapizzi et al, 2020), analysis of biomedical signals (Bakiya et al, 2020), Internet-of-Things domain (Huifeng, Kadry & Raj, 2020;Muthu et al, 2020) and epidemic disease spread forecasting tasks.…”

Human gait analysis for osteoarthritis prediction: a framework of deep learning and kernel extreme learning machine

“…Nowadays, medical and biomedical image processing has attracted a lot of attention due to its importance in health care. [1,2]. The primary goal of medical and biomedical image processing is to detect abnormalities in organs and body of patients [3,4].…”