Open Access

ARTICLE

A Blockchain Based Framework for Stomach Abnormalities Recognition

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

1 Department of Computer Science, HITEC University, Taxila, 47040, Pakistan
2 Department of Computer Science, COMSATS University Islamabad, Wah Campus, 47080, Pakistan
3 College of Computer Science and Engineering, University of Ha’il, Ha’il, Saudi Arabia
4 Department of Mathematics and Computer Science, Beirut Arab University, Lebanon
5 Division of Computer Science, Mathematics and Science, Collins College of Professional Studies, St. John’s University, New York, USA
6 Department of Computer Science and Engineering, Soonchunhyang University, Asan, South Korea

* Corresponding Author: Yunyoung Nam. Email:

(This article belongs to the Special Issue: Innovation of Blockchain Technology)

Computers, Materials & Continua 2021, 67(1), 141-158. https://doi.org/10.32604/cmc.2021.013217

Received 30 July 2020; Accepted 19 October 2020; Issue published 12 January 2021

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 passed in pre-trained deep models. These architectures are modified at each layer, to make them safer and more secure. Each layer contains an extra block, which stores certain information to avoid possible tempering, modification attacks and layer deletions. Information is stored in multiple blocks, i.e., block attached to each layer, a ledger block attached with the network, and a cloud ledger block stored in the cloud storage. After that, features are extracted and fused using a Mode value-based approach and optimized using a Genetic Algorithm along with an entropy function. The Softmax classifier is applied at the end for final classification. Experiments are performed on a private collected dataset and achieve an accuracy of 96.8%. The statistical analysis and individual model comparison show the proposed method’s authenticity.

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Keywords

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