# Region-Based Automated Localization of Colonoscopy and Wireless Capsule Endoscopy Polyps
## 1 Introduction
The comparative analysis has proven that convolutional neural networks (CNN) were providing a state-of-the-art performance. Earlier approaches used feature extraction techniques. Color wavelet covariance (CWC) features along with linear discriminant analysis (LDA) [4] have been used to detect polyps in colonoscopy images. Textural features with support vector machine (SVM) were utilized for texture classification tasks [5]. Color and spatial features [6] and textural features [7] with SVM could outperform the approaches with textural features. Later, deep learning methods were employed to classify the polyp images in colonoscopy videos using CNN [10]. Small patches were extracted to increase the database, and CNN features were used to classify the polyp's presence [11]. Hybrid methods were employed to boost the detection accuracy, such as a combination of edge  
Figure 1: Images with ground truth polyps (marked in green) from colonoscopy videos. Figure 2: Images with ground truth polyps (marked in green) from wireless capsule endoscopy (WCE) videos. detection and feature extraction to filter and refine polyp candidates with a voting scheme [12] to detect the polyps. Training a deep CNN is inappropriate with inadequate data. It has been shown [13] that in medical applications, even for polyp detection, fine-tuning a pre-trained model outperforms models trained from scratch. Another recent work [14], which adopted a Faster region-based convolutional neural network (R-CNN) approach [15] showed an improved performance in the detection of polyps by drawing bounding boxes around polyps and also employing post-learning schemes. Similarly, a VGG16-based Faster R-CNN model [16] was trained on 16 randomly selected sequences from colonoscopy videos. A SegNet-based CNN model [17] was employed to detect polyps using private data that contained 5545 colonoscopy images extracted from 1290 patients. The model was validated and tested on their internally collected colonoscopy image and video data. A news article [18] was also published based on the work from [17]. Various CNN models were trained on 8641 internally collected colonoscopy images, and the models were analyzed through sevenfold cross-validation [19]. A regression-based YOLO (you only look once) detection model was explored for polyp localization [20] on white light and narrow-band polyp images. There are ongoing research studies with WCE videos to detect polyps. Geometric shape features along with textural features [22] were found to be helpful for polyp detection. An SVM-based polyp classification approach was applied with shape, color, and local texture feature extraction [23]. A frame-based binary classification of WCE videos was performed based on geometrical and texture content analysis [24]. Another SVM-based detection with statistical information from red-green-blue (RGB) channels was proposed to determine the polyp presence and extract the radii of the polyps [25]. Texture features integrated with wavelet transform, uniform local binary patterns, and SVM were studied [26]. Although deep learning has many approaches to analyzing natural images, very few works have been conducted on polyp detection in WCE images. A deep learning variant approach with a stacked sparse autoencoder with image manifold constraint [27] was explored to make the model learn and differentiate features from different classes to recognize frames containing polyps from WCE videos. Other methods concentrate on classifying different organs [28] and lesion detection [29] in WCE images. A survey paper [30; 31] on video capsule endoscopy provides a better understanding of various models incorporating the detection and segmentation of polyps in the literature. However, all these methods performed polyp detection or the classification of frame-wise polyp presence. This study mainly focuses on localization through segmentation and locating polyps in both colonoscopy and WCE still frames.