Abstract
The analysis of fecal-type components for clinical diagnosis is important. The main examination involves the counting of red blood cells (RBCs), white blood cells (WBCs), and molds under the microscopic. With the development of machine vision, some vision-based detection schemes have been proposed. However, these methods have a single target for detection, with low detection efficiency and low accuracy. We proposed an algorithm to identify the visible image of fecal composition based on intelligent deep learning. The algorithm mainly includes region proposal and candidate recognition. In the process of segmentation, we proposed a morphology extraction algorithm in a complex background. As for the candidate recognition, we proposed a new convolutional neural network (CNN) architecture based on Inception-v3 and principal component analysis (PCA). This method achieves high-average Precision of 90.7%, which is better than the other mainstream CNN models. Finally, the images within the rectangle marks were obtained. The total time for detection of an image was roughly 1200 ms. The algorithm proposed in the present paper can be integrated into an automatic fecal detection system.
Highlights
Microscopic examination is an important method of clinical testing
For the extraction of region proposal, selective Search (SS) is used for comparison to analyze the advantages of this algorithm in generating candidates in fecal microscopic images
The SS method is more suitable for segmentation and extraction of large targets on small images such as the virtual object classes (VOC) or the ImageNet Large Scale Visual Recognition Challenge (ILSVRC), while the extraction of tangible components such as cells does not apply, as shown in Tables 1 and 2
Summary
Microscopic examination is an important method of clinical testing. Medical staff can determine a patient’s pathological changes based on the fecal routine, by counting the number and the type of cells under a microscope to understand and help analyze and diagnose disease. The majority of small hospitals conduct biological cell detection by manual method. This kind of detection method obviously has the problem of insufficient speed and precision. The rapid identification of the visible components of microscopic cell images in medicine has been the key to the detection of microscopic cells. With the development of machine vision research and improvement in biomedical image processing technology, medical microscopic image processing technology has gradually developed from the traditional, manual recognition method to automated computer identification. With machine vision at the core, image processing technology has become the focus of current research on the automatic identification of visible components of microscopic cell images
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