Recognizing Gastrointestinal Malignancies on WCE and CCE Images by an Ensemble of Deep and Handcrafted Features with Entropy and PCA Based Features Optimization

In medical imaging, automated detection of stomach and gastrointestinal diseases using WCE (wireless capsule endoscopy) images is an emerging research domain. It includes numerous limitations and challenges such as variation in the contrast, texture variation, color and complexity in the background etc. To overcome these challenges, several computer-aided methods are proposed by the researchers. But there exist different limitations in these methods. In this work, a new method is proposed for computer-aided diagnosis of stomach disease classification. This hybrid approach is based on the amassed texture and deep CNN features. Initially, the contrast of image is improved by using power-law transformation. Texture features are extracted from the enhanced dataset by using LBP and SFTA features. Extracted texture features are then fused to obtain strong feature vectors. At the same time, two pre-trained deep learning models are utilized for CNN feature extraction namely VGG16 and InceptionV3. Extracted deep features are fused serially along with the obtained handcrafted feature vector to obtain an ensemble deep feature vector. A unique feature vector is obtained by serial fusion of both fused vectors to get an advantage of accumulated texture and CNN features. This feature vector is supplied to various classifiers and evaluated with existing methods. The promising recognition proficiency portrays the strength of proposed approach.