Combining deep features and hand-crafted features for abnormality detection in WCE images

Abstract

In this paper, a computer-aided method is proposed for abnormality detection Wireless Capsule Endoscopy (WCE) video frames. Common abnormalities in WCE images include ulcers, bleeding, Angiodysplasia, Lymphoid Hyperplasia, and polyp. In this paper, deep features and Hand-crafted features are combined to detect these abnormalities in WCE images. There are no sufficient images to train deep structures therefore the ResNet50 pertained model is used to extract deep features. Hand-crafted features are associated with color, shape, and texture. They are extracted from the region of interest (ROI), i.e. suspicious region. The expectation Maximization (EM) algorithm is used to extract more distinct areas in the background as ROI. The expectation Maximization (EM) algorithm is configured in a way that can extract the area with a distinct texture and color as ROI. The EM algorithm is also initialized with a new fast method which leads to an increase in the accuracy of the method. We also used a novel idea to reveal unexpected color changes in the background due to existing lesions as a feature set. A large number of features are created by the method, so the minimum redundancy maximum relevance approach is used to select a subset of more effective features. These selected features are then fed to a Support Vector Machine for classification. The results show that the proposed approach can detect mentioned abnormalities in WCE frames with the accuracy of 97.82%

Computer-Aided Bleeding Detection Algorithms for Capsule Endoscopy: A Systematic Review

“…Among them, HOG is a popular shape feature extraction method commonly used for object detection and recognition tasks. The studies in [ 119 , 120 , 121 ] specifically utilized the HOG descriptor for shape feature extraction.…”

“…Local feature: A pixel-level feature extraction approach was proposed in several studies in order to accurately identify bleeding images [ 29 , 36 , 51 , 56 , 58 , 120 , 121 , 123 ]. Instead of computing different features from each pixel, a few researchers proposed block-based local feature extraction techniques to reduce time and computational cost [ 37 , 47 ].…”

“…Combined ML & DL algorithms: In recent years, several researchers have employed CNN-based models, such as AlexNet [ 126 ], VGG [ 86 ], ResNet [ 121 , 131 ], InceptionV3 [ 23 ], DenseNet [ 155 ], and XcepNet23 [ 81 ], to extract relevant features from medical images, particularly in tasks like identifying bleeding from normal images. In [ 86 ], deep CNNs (VGG16 and VGG19) were applied to extract features from CE images.…”

Computer-Aided Bleeding Detection Algorithms for Capsule Endoscopy: A Systematic Review

“…In recent years, several researchers have employed CNN-based models, such as AlexNet [126], VGG [86], ResNet [121,131], InceptionV3 [23], DenseNet [155], and XcepNet23 [81], to extract relevant features from medical images, particularly in tasks like identifying bleeding from normal images. In [86], deep CNNs (VGG16 and VGG19) were applied to extract features from CE images.…”