Diagnostic Value of Deep Learning-Based CT Feature for Severe Pulmonary Infection

The study aimed to explore the diagnostic value of computed tomography (CT) images based on cavity convolution U-Net algorithm for patients with severe pulmonary infection. A new lung CT image segmentation algorithm (U-Net+ deep convolution (DC)) was proposed based on U-Net network and compared with convolutional neural network (CNN) algorithm. Then, it was applied to CT image diagnosis of 100 patients with severe lung infection in The Second Affiliated Hospital of Fujian Medical University hospital and compared with traditional methods, and its sensitivity, specificity, and accuracy were compared. It was found that the single training time and loss of U-Net + DC algorithm were reduced by 59.4% and 9.8%, respectively, compared with CNN algorithm, while Dice increased by 3.6%. The lung contour segmented by the proposed model was smooth, which was the closest to the gold standard. Fungal infection, bacterial infection, viral infection, tuberculosis infection, and mixed infection accounted for 28%, 18%, 7%, 7%, and 40%, respectively. 36%, 38%, 26%, 17%, and 20% of the patients had ground-glass shadow, solid shadow, nodule or mass shadow, reticular or linear shadow, and hollow shadow in CT, respectively. The incidence of various CT characteristics in patients with fungal and bacterial infections was statistically significant ( P < 0.05 ). The specificity (94.32%) and accuracy (97.22%) of CT image diagnosis based on U-Net + DC algorithm were significantly higher than traditional diagnostic method (75.74% and 74.23%), and the differences were statistically significant ( P < 0.05 ). The network of the algorithm in this study demonstrated excellent image segmentation effect. The CT image based on the U-Net + DC algorithm can be used for the diagnosis of patients with severe pulmonary infection, with high diagnostic value.

Analysis of Curative Effect and Prognostic Factors of Radiotherapy for Esophageal Cancer Based on the CNN

An esophageal cancer intelligent diagnosis system is developed to improve the recognition rate of esophageal cancer image diagnosis and the efficiency of physicians, as well as to improve the level of esophageal cancer image diagnosis in primary care institutions. In this paper, by collecting medical images related to esophageal cancer over the years, we establish an intelligent diagnosis system based on the convolutional neural network for esophageal cancer images through the steps of data annotation, image preprocessing, data enhancement, and deep learning to assist doctors in intelligent diagnosis. The convolutional neural network-based esophageal cancer image intelligent diagnosis system has been successfully applied in hospitals and widely praised by frontline doctors. This system is beneficial for primary care physicians to improve the overall accuracy of esophageal cancer diagnosis and reduce the risk of death of esophageal cancer patients. We also analyze that the efficacy of radiation therapy for esophageal cancer can be influenced by many factors, and clinical attention should be paid to grasp the relevant factors in order to improve the final treatment effect and prognosis of patients.

Medical Image Segmentation by Fish Schooling Algorithm and Neural Network

Medical image diagnosis by machine decrease the doctor load and increases the efficiency of treatment as well. Many of diagnosis process depends on chemical data and some are depend on digital images. This work focus on brain tumor medical image diagnosis by segmenting the tumor region in the image. For tumor detection neural network was trained by the model. Selected features extract from the image by fish schooling genetic algorithm for training of neural network It was obtained that fish schooling based genetic feature selection has increases the detection accuracy of trained model. Experiment was done on real dataset and results compared with existing techniques of tumor detection from MRI images.

Performance Analysis on the Effect of Noise in Inverse Surface Adaptive Thresholding (ISAT)

Thresholding is one of the powerful methods in segmentation phase. Numerous methods were proposed to segment the foreground from the background but there is limited number of studies that analyse the effect of noise since the present of noise will affect the performance of the thresholding method. In this paper, the main idea is to analyse the effect of noise in Inverse Surface Adaptive Thresholding (ISAT) method. ISAT method is known as an excellent method to segment the image with the present of noise. The result of this analysis can be a guideline to researcher when implementing ISAT method especially in medical image diagnosis. Initially, several images with different noise variations were prepared and underwent ISAT method. In ISAT method, several image processing methods were incorporated namely edge detection, Otsu thresholding and inverse surface construction. The resulting images were evaluated using Misclassification Error (ME) to evaluate the performance of the segmentation result. Based on the obtained results, ISAT performance is consistent although the noise percentage increases from 5% to 25%.