This paper performs a comprehensive study on the computer-aided detection for the medical diagnosis with deep learning. Based on the region convolution neural network and the prior knowledge of target, this algorithm uses the region proposal network, the region of interest pooling strategy, introduces the multi-task loss function: classification loss, bounding box localization loss and object rotation loss, and optimizes it by end-to-end. For medical image it locates the target automatically, and provides the localization result for the next stage task of segmentation. For the detection of left ventricular in echocardiography, proposed additional landmarks such as mitral annulus, endocardial pad and apical position, were used to estimate the left ventricular posture effectively. In order to verify the robustness and effectiveness of the algorithm, the experimental data of ultrasonic and nuclear magnetic resonance images are selected. Experimental results show that the algorithm is fast, accurate and effective.
In order to study the effect of light with different wavelengths on the motion behavior of carp robots, phototaxis experiment, anatomical experiment, light control experiment and speed measurement experiment were carried out in this study. Blue, green, yellow and red light with different wavelength were used to conduct phototaxis experiments on carp to observe their movement behavior. By dissecting the skull bones of the carp to determine the appropriate location to carry the light control device, we independently developed a light control carrying device which was suitable for any illumination intensity environment. The experiment of the light-controlled carp robots was carried out. The motion behavior of the carp robot was checked by using computer binocular stereo vision technology. The motion trajectory of the carp robot was tracked and obtained by applying kernel correlation filter (KCF) algorithm. The motion velocity of the carp robot at different wavelengths was calculated according to their motion trajectory. The results showed that carps’ sensitivity to different light changed from strong to weak in the order of blue, red, yellow and green, so that using light with different wavelengths to control the speed of the carp robot has certain laws to follow. A new method to avoid brain damage in carp robots control can be provided in this study.
Otitis media is one of the common ear diseases, and its accurate diagnosis can prevent the deterioration of conductive hearing loss and avoid the overuse of antibiotics. At present, the diagnosis of otitis media mainly relies on the doctor's visual inspection based on the images fed back by the otoscope equipment. Due to the quality of otoscope equipment pictures and the doctor's diagnosis experience, this subjective examination has a relatively high rate of misdiagnosis. In response to this problem, this paper proposes the use of faster region convolutional neural networks to analyze clinically collected digital otoscope pictures. First, through image data enhancement and preprocessing, the number of samples in the clinical otoscope dataset was expanded. Then, according to the characteristics of the otoscope picture, the convolutional neural network was selected for feature extraction, and the feature pyramid network was added for multi-scale feature extraction to enhance the detection ability. Finally, a faster region convolutional neural network with anchor size optimization and hyperparameter adjustment was used for identification, and the effectiveness of the method was tested through a randomly selected test set. The results showed that the overall recognition accuracy of otoscope pictures in the test samples reached 91.43%. The above studies show that the proposed method effectively improves the accuracy of otoscope picture classification, and is expected to assist clinical diagnosis.