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Wavelet Transform and Deep Convolutional Neural Network-Based Smart Healthcare System for Gastrointestinal Disease Detection

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Abstract

This work presents a smart healthcare system for the detection of various abnormalities present in the gastrointestinal (GI) region with the help of time–frequency analysis and convolutional neural network. In this regard, the KVASIR V2 dataset comprising of eight classes of GI-tract images such as Normal cecum, Normal pylorus, Normal Z-line, Esophagitis, Polyps, Ulcerative Colitis, Dyed and lifted polyp, and Dyed resection margins are used for training and validation. The initial phase of the work involves an image pre-processing step, followed by the extraction of approximate discrete wavelet transform coefficients. Each class of decomposed images is later given as input to a couple of considered convolutional neural network (CNN) models for training and testing in two different classification levels to recognize its predicted value. Afterward, the classification performance is measured through the following measuring indices: accuracy, precision, recall, specificity, and F1 score. The experimental result shows 97.25% and 93.75% of accuracy in the first level and second level of classification, respectively. Lastly, a comparative performance analysis is carried out with several other previously published works on a similar dataset where the proposed approach performs better than its contemporary methods.

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Abbreviations

GI:

Gastrointestinal

CNN:

Convolutional neural network

AI:

Artificial intelligence

WT:

Wavelet transform

2D-DWT:

Two-dimensional discrete wavelet transform

TF:

Time–frequency

CWT:

Continuous wavelet transform

α :

Scaling parameter

t :

Translation parameter

\(A\left( n \right)\) :

Approximate coefficients

\(D\left( n \right)\) :

Detail coefficients

\(g(n)\) :

Low-pass filter

\(h(n)\) :

High-pass filter

MRA:

Multi-resolution analysis

L:

Low pass

H:

High pass

ReLU:

Rectified linear unit

L(α):

Loss function

A:

Accuracy

R:

Recall

P:

Precision

F1:

F1 Score

S:

Specificity

TD:

Truly detected

FD:

Falsely detected

IPP:

Image pre-processing

BF:

Baseline features

ANN:

Artificial neural network

SVM:

Support vector machine

RF:

Random forest

BMFA:

Bidirectional marginal Fisher analysis

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Siksha O Anusandhan (Deemed to Be University), Bhubaneswar, 751030, India

    Subhashree Mohapatra

  2. Department of Computer Science, Aditya Institute of Technology and Management, Srikakulam, Andhra Pradesh, 532201, India

    Janmenjoy Nayak

  3. Department of Electrical and Electronics Engineering, Siksha O Anusandhan Deemed to Be University, Bhubaneswar, 751030, India

    Manohar Mishra

  4. Department of Gastroenterology, Institute of Medical Science, SUM Hospital, Bhubaneswar, 751030, India

    Girish Kumar Pati

  5. Department of Computer Application, Veer Surendra Sai University of Technology, Burla, 768018, India

    Bignaraj Naik

  6. Department of Computer Application, Siksha O Anusandhan (Deemed to Be University), Bhubaneswar, 751030, India

    Tripti Swarnkar

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  1. Subhashree Mohapatra
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Correspondence to Janmenjoy Nayak.

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Mohapatra, S., Nayak, J., Mishra, M. et al. Wavelet Transform and Deep Convolutional Neural Network-Based Smart Healthcare System for Gastrointestinal Disease Detection. Interdiscip Sci Comput Life Sci 13, 212–228 (2021). https://doi.org/10.1007/s12539-021-00417-8

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