|
Gastrointestinal diseases are one of the important causes of gastrointestinal tumors, and also one of the serious public health problems that human beings are currently facing. Gastrointestinal inflammation and polyp lesions are the main ways to cause gastrointestinal cancer. Therefore, timely detection of gastrointestinal polyps and inflammation and other lesions through gastrointestinal endoscopy can effectively prevent corresponding cancer. In recent years, deep learning technology has played a huge role in computer-aided gastrointestinal endoscopic diagnosis, especially the convolutional neural network can recognize gastrointestinal lesions. This paper uses the public gastrointestinal endoscopy data set Kvasir (including 7 categories, 1000 images for each category), proposed and designed CBAM-AlexNet deep learning model, in which CBAM is the attention mechanism part, including channel attention and spatial attention, AlexNet is a deep learning network. The original features are input into the channel attention mechanism in CBAM to extract channel features, and then the output channel features are input into the spatial attention mechanism to extract spatial features, and finally the output spatial features are input into the AlexNet network for feature extraction and gastrointestinal disease data. The average accuracy rate, average accuracy rate, average recall rate and average F1Score of all categories of CBAM-AlexNet model were 91.36%, 91.34%, 91.39% and 0.913, respectively, and achieved good classification effect in seven categories of gastrointestinal endoscopy data, showing the superiority of the model. CBAM-AlexNet model has high specificity and sensitivity for the recognition of gastrointestinal polyps, which can assist doctors in the clinical diagnosis of gastrointestinal polyps quickly and reduce the probability of misdiagnosis, Some recognition errors caused by quality issues such as overexposure or underexposure of images will be improved in future work.
|
