## 1 Introduction
In the segmentation method, the tested image is basically segmented into several regions with similar features. The objectives of segmentation in a medical image are usually to analyze an anatomical structure, locate the texture region of interest, and measure the tissue volume [26]. Several researchers applied deep learning-basedsegmentation to extract the medical images of the prostate [27], lung [28], skin cancer [28], brain [29], and pancreas [30]. [PERSON] et al. proposed the Convolutional Neural Network (CNN) model in predicting 3D image segmentation for the whole prostate volume [27]. [PERSON] et al. [28] used the RU-Net and R2U-Net models, which take advantage of the U-Net, Residual Network, and RCNN model features, and successfully tested them for the segmentation of the blood vessel in retinal images, skin cancer, and lung lesion. [PERSON] et al. [29] presented a fully connected CNN model in a brain tumor segmentation. [PERSON] et al. [30] applied the ConvNet model for pancreas segmentation, and the results showed that the superpixels can be classified into the pancreas and non-pancreas types. Image semantic segmentation methods have been used in the precision agriculture. [PERSON] et al. [31] utilized a deep convolutional network with an encoder-decoder in fig plant segmentation, which classified each pixel as a crop or non-crop of the raw images, and the experimental results showed a mean accuracy of 93.85%. [PERSON] et al. [32] designed the network architecture of combining the Segnet and Enet to separate sugar beet plants, weeds, and background images. Presently, deep learning-based classification and segmentation have proven to be superior in decision making applied to the understanding of some certain medical images by a professional. As for the deep learning-based classification task, the main purpose is to automatically divide medical images into different groups, which is beneficial for doctors to provide effective diagnosis. The deep learning models are widely applied in medical area studies, including tumor classification [33] and breast cancer detection [34]. [PERSON] et al. [33] utilized a deep neural network classifier, discrete wavelet transform, and principal component analysis to classify 66 brain MRIs into four tumor types. [PERSON] et al. [34] used the ConvNet classifier to detect different types of invasive breast cancer for the whole slide digitized pathology images. In view of the aforementioned studies, the classification and segmentation approaches can effectively assist resectoscope image detection. In [35], [PERSON] et al. proposed a multi-network model which used the pre-trained ResNet-50 model to extract features and trained an E-SVM model to classify the breast cancer images into benign, in situ carcinomas, invasive carcinomas, and normal. [PERSON] et al. [36] proposed the residual neural networks with DST-ResNet and EDST-ResNet to automatically detect and grade the cataract. [PERSON] et al. [37] proposed a multi-channel ResNet framework, which combined multiple ResNets models, to classify four types of skin diseases. According to the above literatures, the ResNet50 model has been applied in medical image classification applications successfully. As bleeding is the most common phenomenon in the TURP surgery, this investigation mainly focuses on bleeding detection. For the overview of the literature research [38], the color spaces of RGB and hue, saturation, and value (HSV) are the main approaches used to detect the bleeding region. In an RGB image, each pixel was valued by three 8-bit numbers corresponding to the red, green, and blue planes. The bleeding image was represented by the pixel characteristics of the red color component in the RGB color space. In [39], researchers used the variation of pixel intensities in the RGB color planes to detect the bleeding image. The statistical parameters of mean, mode, maximum, minimum, skewness, median, variance, and kurtosis were used to extract the bleeding characteristics of the R and G intensity planes [40]. [PERSON] et al.