Wavelet Transform and Deep Convolutional Neural Network-Based Smart Healthcare System for Gastrointestinal Disease Detection.
Interdiscip Sci
; 13(2): 212-228, 2021 Jun.
Article
de En
| MEDLINE
| ID: mdl-33566337
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.
Mots clés
Texte intégral:
1
Collection:
01-internacional
Base de données:
MEDLINE
Sujet principal:
Analyse en ondelettes
/
Maladies gastro-intestinales
Type d'étude:
Diagnostic_studies
/
Prognostic_studies
Limites:
Humans
Langue:
En
Journal:
Interdiscip Sci
Sujet du journal:
BIOLOGIA
Année:
2021
Type de document:
Article
Pays d'affiliation:
Inde