Talha Saeed, Chu Kiong Loo^*, Muhammad Shahreeza Safiruz Kassim

CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 4357-4372, 2022, DOI:10.32604/cmc.2022.019076

Abstract

Abnormalities of the gastrointestinal tract are widespread worldwide today. Generally, an effective way to diagnose these life-threatening diseases is based on endoscopy, which comprises a vast number of images. However, the main challenge in this area is that the process is time-consuming and fatiguing for a gastroenterologist to examine every image in the set. Thus, this led to the rise of studies on designing AI-based systems to assist physicians in the diagnosis. In several medical imaging tasks, deep learning methods, especially convolutional neural networks (CNNs), have contributed to the state-of-the-art outcomes, where the complicated nonlinear relation between target classes and… More >

Javeria Naz¹, Muhammad Attique Khan¹, Majed Alhaisoni², Oh-Young Song^3,*, Usman Tariq⁴, Seifedine Kadry⁵

CMC-Computers, Materials & Continua, Vol.69, No.1, pp. 607-625, 2021, DOI:10.32604/cmc.2021.017101

Abstract An automated system is proposed for the detection and classification of GI abnormalities. The proposed method operates under two pipeline procedures: (a) segmentation of the bleeding infection region and (b) classification of GI abnormalities by deep learning. The first bleeding region is segmented using a hybrid approach. The threshold is applied to each channel extracted from the original RGB image. Later, all channels are merged through mutual information and pixel-based techniques. As a result, the image is segmented. Texture and deep learning features are extracted in the proposed classification task. The transfer learning (TL) approach is used for the extraction… More >

Muhammad Attique Khan¹, Inzamam Mashood Nasir¹, Muhammad Sharif², Majed Alhaisoni³, Seifedine Kadry⁴, Syed Ahmad Chan Bukhari⁵, Yunyoung Nam^6,*

CMC-Computers, Materials & Continua, Vol.67, No.1, pp. 141-158, 2021, DOI:10.32604/cmc.2021.013217

Abstract Wireless Capsule Endoscopy (WCE) is an imaging technology, widely used in medical imaging for stomach infection recognition. However, a one patient procedure takes almost seven to eight minutes and approximately 57,000 frames are captured. The privacy of patients is very important and manual inspection is time consuming and costly. Therefore, an automated system for recognition of stomach infections from WCE frames is always needed. An existing block chain-based approach is employed in a convolutional neural network model to secure the network for accurate recognition of stomach infections such as ulcer and bleeding. Initially, images are normalized in fixed dimension and… More >