Search

This paper proposed an intelligent plant disease detection system that uses the modified Alexnet. As a convolutional neural network (CNN), Alexnet is good at objection detection from images. However, plant disease detection by the leaf image is a difficult task compared to traditional object detection. Therefore, Alexnet is modified for the different size images and angles to fit the requirement of a practical scenario. A training corpus gathered from the internet and with human leveling is used to train the detection models. According to the experimental results, the proposed approach achieves a good performance.

Deep learning technology is widely used in medicine. The automation of medical image classification and segmentation is essential and inevitable. This study proposes a transfer learning–based kidney segmentation model with an encoder–decoder architecture. Transfer learning was introduced through the utilization of the parameters from other organ segmentation models as the initial input parameters. The results indicated that the transfer learning–based method outperforms the single-organ segmentation model. Experiments with different encoders, such as ResNet-50 and VGG-16, were implemented under the same Unet structure. The proposed method using transfer learning under the ResNet-50 encoder achieved the best Dice score of 0.9689. The proposed model’s use of two public data sets from online competitions means that it requires fewer computing resources. The difference in Dice scores between our model and 3D Unet (Isensee) was less than 1%. The average difference between the estimated kidney volume and the ground truth was only 1.4%, reflecting a seven times higher accuracy than that of conventional kidney volume estimation in clinical medicine.

The main purpose of this study is to build a MRI‐based 3D tongue atlas for research on tongue morphometrics and physiological modeling of normal and disordered speech production. In this study, MRI data of 8 subjects (4 males and 4 females) were chosen from an orally‐based MRI database of 20 male and 20 female college students without speech disorders. Sixteen landmarks were defined and selected from the 3D reconstructed MRI tongue images based on the subjects. Thin‐plate spline analysis (TPS) was used to build a 3D tongue atlas for male and female subjects, respectively. Sagittal sections of the original MRI data were used to evaluate the accuracy of image registration and reconstruction. The resulted 3D tongue atlas was used to study subject‐to subject, subject‐to‐atlas, and male‐to‐female morphometric variation. Preliminary results show the major difference among female subjects before and after the TPS analysis is in the area of tongue dorsum that is close to the velum and epiglottis, respectively. Ho...