Abstract:
Recently, gastrointestinal(GI) tract disease diagnosis
through endoscopic image classification is an active research area
in the biomedical field. Several GI tract disease classification
methods based on image processing and machine learning techniques
have been proposed by diverse research groups in the
recent past. However, yet effective and comprehensive deep ensemble
neural network-based classification model is not available
in the literature. In this research work, we propose to use an
ensemble of deep features as a single feature vector by combining
pre trained DenseNet-201, ResNet-18, and VGG-16 CNN models
as the feature extractors followed by a global average pooling
(GAP) layer to predict eight-class anomalies of the digestive
tract diseases. Our results show a promising accuracy of over
97% which is a remarkable performance with respect to the
state-of-the-art approaches. We analyzed how prominent CNN
architectures that have appeared recently (DenseNet, ResNet,
Xception, InceptionV3, InceptionResNetV2, and VGG) that can
be used for the task of transfer learning. Furthermore, we
describe a technique of reducing processing time and memory
consumption while preserving the accuracy of the classification
model by using feature extraction based on SVD.
