Multiclass Stomach Diseases.pdf (2.06 MB)
Download file

Multiclass Stomach Diseases Classification Using Deep Learning Features Optimization

Download (2.06 MB)
journal contribution
posted on 11.05.2021, 10:40 by MA Khan, A Majid, N Hussain, M Alhaisoni, YD Zhang, S Kadry, Y Nam
In the area of medical image processing, stomach cancer is one of the most important cancers which need to be diagnose at the early stage. In this paper, an optimized deep learning method is presented for multiple stomach disease classification. The proposed method work in few important steps—preprocessing using the fusion of filtering images along with Ant Colony Optimization (ACO), deep transfer learning-based features extraction, optimization of deep extracted features using nature-inspired algorithms, and finally fusion of optimal vectors and classification using Multi-Layered Perceptron Neural Network (MLNN). In the feature extraction step, pre-trained Inception V3 is utilized and retrained on selected stomach infection classes using the deep transfer learning step. Later on, the activation function is applied to Global Average Pool (GAP) for feature extraction. However, the extracted features are optimized through two different nature-inspired algorithms—Particle Swarm Optimization (PSO) with dynamic fitness function and Crow Search Algorithm (CSA). Hence, both methods’ output is fused by a maximal value approach and classified the fused feature vector by MLNN. Two datasets are used to evaluate the proposed method—CUI WahStomach Diseases and Combined dataset and achieved an average accuracy of 99.5%. The comparison with existing techniques, it is shown that the proposed method shows significant performance.

History

Citation

Computers, Materials & Continua, vol. 67, no.3, pp. 3381–3399, 2021.

Author affiliation

Department of Informatics

Version

VoR (Version of Record)

Published in

Computers, Materials and Continua

Volume

67

Issue

3

Pagination

3381 - 3391

Publisher

Tech Science Press

issn

1546-2218

eissn

1546-2226

Acceptance date

16/12/2020

Copyright date

2021

Available date

11/05/2021

Language

English