Automatic chronic degenerative diseases identification using enteric nervous system images

Abstract

Studies recently accomplished on the Enteric Nervous System have shown that chronic degenerative diseases affect the Enteric Glial Cells (EGC) and, thus, the development of recognition methods able to identify whether or not the EGC are affected by these type of diseases may be helpful in its diagnoses. In this work, we propose the use of pattern recognition and machine learning techniques to evaluate if a given animal EGC image was obtained from a healthy individual or one affect by a chronic degenerative disease. In the proposed approach, we have performed the classification task with handcrafted features and deep learning-based techniques, also known as non-handcrafted features. The handcrafted features were obtained from the textural content of the ECG images using texture descriptors, such as the Local Binary Pattern (LBP). Moreover, the representation learning techniques employed in the approach are based on different Convolutional Neural Network (CNN) architectures, such as AlexNet and VGG16, with and without transfer learning. The complementarity between the handcrafted and non-handcrafted features was also evaluated with late fusion techniques. The datasets of EGC images used in the experiments, which are also contributions of this paper, are composed of three different chronic degenerative diseases: Cancer, Diabetes Mellitus, and Rheumatoid Arthritis. The experimental results, supported by statistical analysis, show that the proposed approach can distinguish healthy cells from the sick ones with a recognition rate of 89.30% (Rheumatoid Arthritis), 98.45% (Cancer), and 95.13% (Diabetes Mellitus), being achieved by combining classifiers obtained on both feature scenarios.

Research on Automatic Recognition Technology of Library Books Based on Image Processing

“…The first is to increase the inclusion of the Sobel operator relative to the edge direction. Due to the different edge directions, 6 templates with different edges moving 45 degrees clockwise can be added [12]. Secondly, the Sobel operator algorithm is improved, that is, the result of the S convolution operation on the 8 modes M1-M8: S1 = a1+2a8+a7-a3-2a4-a5.…”

“…In addition, some local edges with slow changes in gray value will be lost, so this algorithm may also have the defect of false edges or edge loss. The canny operator is recognized as an operator with a low Gaussian filtering is performed on the image, that is, the specified o, x, and y are taken to obtain the corresponding Gaussian kernel, which is convolved with the image [12]. On this basis, the partial derivative is used to calculate the gradient amplitude and gradient direction as shown in formulas ( 9) and ( 10):…”

“…Among them, the calculation of the partial derivative can be obtained by the first-order difference approximation. The gradient operator used by the canny operator is shown in formulas (11), (12), and (13):…”