Image Processing Method Based on FGCA and Artificial Neural Network

Abstract

With the aggravation of human sub-health problems, people’s demand for medical assistance is increasing. In the face of an endless stream of diseases, doctors use medical image analysis to intuitively obtain the morphological information of the affected part of the disease, which is convenient for doctors to make a more accurate assessment of the disease. The processing of medical images is essential for the treatment of people’s diseases and subsequent observation and recovery. Therefore, it is necessary to pay attention to the development and innovation of image processing. With the continuous increase of diseases and the innovation and development of technology, the existing medical image processing technology still has problems such as noise and low image contrast. The application of fuzzy genetic clustering algorithms and artificial neural networks can help image processing be more accurate and perfect. In view of the above problems, this paper has carried out data analysis and research on image processing methods based on fuzzy genetic clustering algorithm (FGCA) and artificial neural network (ANN). The research results have shown that in the case of no noise and 5% salt and pepper noise, the FGCA segmentation coefficient is the largest, at 0.9756 and 0.9758, respectively, and the segmentation entropy is the smallest, at 0.0885 and 0.0925, respectively. The liver CT (Computer Tomography) image segmentation method based on DeepLab V3+ has the highest PA, Mlou value, and Dice coefficient, which are 88%, 95%, and 94%, respectively, which has laid the foundation for the innovation and development of image processing methods.

Optimization and Performance Evaluation of Deep Learning Algorithm in Medical Image Processing

“…If high-precision segmentation results are needed, DenseNet model can be considered. If the performance requirements are loose, you can choose U-Net model; The DeepLab model can be used as a compromise choice [22][23]. In addition, according to the performance differences of different models, we can further explore the adjustment of model structure and parameters to improve performance.…”

Biometric System for Person Authentication Using Retinal Vascular Branching Pattern

“…The formation of feature vectors is obtained by converting the segmented retinal image into a numerical value so that at the original scale the retinal image results from blood vessel segmentation will form a large input vector size. The rescaling operation is performed based on the mean pixel value [26] in the segmented vein pattern which results in one average pixel value per segment with the average of each R-segment in the segmented image calculated using Eq. ( 5) [14]:…”