Semi-supervised Method for Image Texture Classification of Pituitary Tumors via CycleGAN and Optimized Feature Extraction
Background: Accurately determining the softness level of pituitary tumors preoperatively by using their image textures can provide a basis for surgical options and prognosis. Existing methods for this problem require manual intervention, which could hinder the efficiency and accuracy considerably. Methods: we present an automatic method for diagnosing the texture of pituitary tumors using unbalanced sequence image data. Firstly, for the small sample problem in our pituitary tumor MRI image
... umor MRI image dataset where T1 and T2 sequence data are unbalanced (due to data missing) and under-sampled, our method uses a CycleGAN model for domain conversion to obtain fully sampled MRI spatial sequence. Then, it uses a DenseNet-ResNet based Autoencoder framework to optimize the feature extraction process for pituitary tumor image data. Finally, to take advantage of sequence data, it uses a CRNN model to classify pituitary tumors based on their predicted softness levels. Results: Experiments show that our method is the best in terms of efficiency and accuracy(91.78%) compared to other methods. Conclusions: We propose a semi-supervised method for grading pituitary tumor texture. This method can accurately determine the softness level of pituitary tumors, which provides convenience for surgical selection and prognosis, and improves the diagnostic efficiency of pituitary tumors.