# An Improved Method of Polyp Detection Using Custom YOLOv4-Tiny
## 2 Related Work  
Existing research on medical image analysis, particularly wireless endoscopic image analysis, is widely used for the detection of bleeding [10], polyp [1, 22], malignancy [2], ulcers [23], and gastrointestinal tumors [24]. Color-based augmentation plays a crucial role in converting RGB images [10]. Shape and texture analyses show different performances for different disease classes, such as polyps, ulcers, and tumors. Wavelet transform [25], and a bag of visual words [26] are examples of new approaches that use various types of data features. Several threshold-based image classification techniques using ML algorithms have been developed [13, 14, 27]. Deep sparse SVM (DSSVM) [28] has been developed for identifying abnormal frames. The DSSVM is trained on color and texture features extracted from image super-pixels. Recently, deep learning has greatly improved medical diagnosis, with the most commonly used CNN models, such as AlexNet [11], Google Net [13], and VGG16 [12], used for wireless endoscopic image analysis for the early detection of endoscopic diseases. Deep learning techniques for automatic detection of intestinal bleeding from capsule endoscopy images [10] used the SegNet [13] architecture for the segmentation of the disease area in endoscopic images, particularly the bleeding area. The model could detect only a single class, bleeding, from endoscopic images; it scored above 90% accuracy in segmenting areas of bleeding in endoscopic images. Another related study developed a method for the automatic detection of bowel diseases in endoscopy images [7]. This method was divided into two main categories, detection and classification, for diagnosing gastrointestinal bleeding, polyps, and tumors. Feature extraction techniques, such as the Laplacian of Gaussian, were used for detection, and multilayer perceptron based on back-propagation was used for classification. This method performed well with a multilayer perceptron neural network with no prior knowledge of the statistical distribution. For practical implementation, the goal should have been to collect a more accurate dataset to obtain a high-performance score. A CNN model [22] was developed for polyp recognition from endoscopy images. This model used a stacked auto-encoder with an image manifold constraint to recognize polyps. Furthermore, they tried asymmetrical and unsupervised learning techniques for learning image features automatically; however, the model had low performance because of overfitting. A similar work [27] developed an enhanced CNN-based gaze estimation model for wireless capsule endoscopy images, in which an auto-encoder CNN model predicted visual saliency map estimation; this reconstructed model efficiently detected Gaza patterns. Diagnosis from medical images has always been a difficult task because it requires detection, localization, classification, and other related tasks. While using wireless endoscopic images, a hybrid model was used to obtain accurate results in the early detection of polyps that could cause cancer, and in the classification of gastrointestinal diseases. A CNN model for the real-time detection of early esophageal neoplasia [24] was developed using Inception-ResNetv2 for feature extraction and YOLOv2 [29] for lesion detection. Between 2016 and 2018, electronic endoscopy was used to collect 916 images from 30 patients. The model identified 24 of the 26 patients as having dysplasia with 92.3% accuracy; however, it was not tested on other real endoscopic images and showed low performance in real-time multi-detection of the disease. ResUNet++ was applied successfully in polyp segmentation [30], and conditional random field (a class of statistical models) and test-time augmentation [18]. Deep learning architecture has achieved high performance in wireless endoscopic image analysis. The main goal of these models is related to the segmentation of the polyp area. They used cross-date testing techniques to determine the capacity of the model.