An Expanded MLP Neural Network for Fast Angiodysplasia Lesions Segmentation in Capsule Endoscopy Images

In this paper, an expanded multilayer perceptron (EMLP) neural network is proposed to automatically segment angiodysplasia regions in wireless capsule endoscopy (WCE) images The main idea is to minimize the distance between the input image and the corresponding binary ground truth, i.e., the mask image. After the training phase, when a test image is given to the network, the lesion pixels will be close to "one" and the other pixels will be close to "zero" and finally, the lesion area can be segmented using thresholding. Since angiodysplasia lesions appear in images with different spectrums of red color, the classical MLP neural network cannot be trained with a wide range of red color, hence leads to undesirable network accuracy. To solve this problem, we proposed an EMLP neural network for image segmentation. In the EMLP neural network, neurons are divided into several groups, each of which is for learning a spectrum of the lesion. The EMLP is able to learn a wider range of red colors. The proposed method is able to segment WCE images containing angiodysplasia faster than the existing methods. Our investigation shows that our method also outperforms existing methods in terms of segmentation scores.