# An Improved Method of Polyp Detection Using Custom YOLOv4-Tiny
## 1 Introduction  
The importance of the early detection and diagnosis of dangerous diseases has risen sharply in medical healthcare and, simultaneously, AI-based diagnostic systems have had a tremendous positive impact on the real-time detection and localization of disease areas. Among several reliable methods, Computer Vision (CV) techniques have also become common in real-time detection. Gastrointestinal cancer is very dangerous and common among patients, and early detection of the cancer using a high-quality, reliable AI-based model is crucial. Learning of Wireless Endoscopic Images allows detection of even small diseases to avoid dangerous effects. Most studies are related to computer vision-based diagnostic tools for the detection of tumors, polyps [1], malignancy [2], and ulcers [3]. Polyps are particularly difficult to detect [1, 4], since they are smaller than 10 mm and have been shown to be most missed during diagnosis [5]. There is a high demand for endoscopic examination and it requires both equipment and qualified experts for diagnostic analysis for early detection, which may result in a better understanding of cancer screening and reduce deaths due to cancer. Over the past few years, new methods for reducing human error have been developed [6].  
Some of the more recent research has already achieved high-quality results for both the real-time endoscopic detection and classification of cancer using several techniques, such as machine learning (ML) [7] techniques, used in wireless capsule endoscopy (WCE). Furthermore, pixel-based models, such as bleeding-detection models, have also been effectively used in the GI tract [8]. Deep learning is becoming widely recognized as a powerful technique and several deep learning methods [9, 10] have been developed, such as AlexNet [11] and VGG16 [12] for detecting bleeding regions, GoogleNet [13] for polyp classification,and Inceptionv4 [14] for polyp localization. Several image-segmentation models have also been developed for polyp segmentation [15] and pancreas segmentation [16]. The U-Net network, with a typical encoder and decoder, was the main model used in medical image segmentation. UNet++ [17] was developed by adding an extra layer to reduce the number of features, which resulted in improved disease segmentation. With the increasing significance of polyp classification, ResUNet++ [18] was developed as a U-Net-shaped network with a residual block.  
Recently, hybrid models have emerged as a promising method for the detection and classification of tumors. These models include not only the localization of abnormal areas, but also the detection of bleeding, lesions, and polyps. Techniques based on computer vision and deep learning, such as Grad-Cam++ [19] and custom SegNet [20], have achieved good results by using a convolutional neural network (CNN) classifier before the segmentation of the disease area.  
In this study, we developed a new custom object detection model, which is a modified version of the YOLOv4-tiny model, where the Inception-ResNet-A block replaced the backbone architecture of the YOLOv4-tiny, is replaced with a new block network for high-accuracy disease classification and disease detection. In addition, since we used the small open-source KVASIR [21] dataset, we applied efficient data augmentation operations to enhance the dataset.