Abstract
The performance characteristics of deep learning fully convolutional neural network (DLFCNN) algorithm-based computed tomography (CT) images were investigated in the detection and diagnosis of perianal abscess tissue. 60 patients who were medically diagnosed as perianal abscesses in the hospital were selected as the experimental group, and 60 healthy volunteers were selected as the control group. In this study, the DLFCNN algorithm based on deep learning was compared with the CNN algorithm and applied to the segmentation training of CT images of patients with perianal abscesses. Then, the segmentation metrics Jaccard, Dice coefficient, precision rate, and recall rate were compared by extracting the region of interest. The results showed that Jaccard (0.7326) calculated by the CNN algorithm was sharply lower than that of the DLFCNN algorithm (0.8525), and the Dice coefficient (0.7264) was also steeply lower than that of the DLFCNN algorithm (0.8434) (P < 0.05). The thickness range of the epidermis and dermis in patients from the experimental group was 4.1–4.9 mm, which was markedly greater than the range of the control group (1.8–3.6 mm) (P < 0.05). Besides, the CT value of the subcutaneous fascia in the experimental group (−95.45 ± 8.26) hugely reduced compared with the control group (−76.34 ± 7.69) (P < 0.05). The accuracy rate of the patients with perianal abscesses was 96.67% by multislice spiral CT (MSCT). Therefore, the DLFCNN algorithm in this study had good stability and good segmentation effect. The skin at the focal site of anal abscess was obviously thickened, and it was simple and accurate to use CT images in the diagnosis of patients with perianal abscesses, which could effectively locate the lesion and clarify the relationship between the lesion and the surrounding structure.
Highlights
Perianal region refers to the area extending 5-6 cm from the junction between the anal squamous mucosa and the skin [1]
Another 60 healthy volunteers were selected as the control group, and the experimental group was diagnosed by multislice spiral computed tomography (MSCT) scanning. e study was approved by the Medical Ethics Committee of the hospital, and the research objects and their family members learnt about the study and signed informed consent forms
The deep learning fully convolutional neural network (DLFCNN) algorithm was proposed based on deep learning and compared with the CNN algorithm, which were applied in the computed tomography (CT) image evaluation of 60 patients with perianal abscesses
Summary
Perianal region refers to the area extending 5-6 cm from the junction between the anal squamous mucosa and the skin [1]. Perianal abscess is a common and frequently occurring disease in surgery, which has a high incidence and seriously affects the normal life and work of patients. The comprehensive diagnosis of perianal abscess was mainly made by digital surgical rectal examination, clinical symptoms, clinical vital signs, etc., which could not directly determine the location and range of the lesion and resulted in high blindness in diagnosis and treatment [4]. Erefore, the surgical treatment of perianal abscess has to be accurately positioned and diagnosed preoperatively to improve the safety of the surgery. Erefore, the application of CT scanning to examine the tissue structure of human perianal abscesses will help improve the clinical diagnosis of anal diseases CT scanning is used for the examination of the human abdomen and pelvis, without special bowel preparation such as enema or inflation [7]. erefore, the application of CT scanning to examine the tissue structure of human perianal abscesses will help improve the clinical diagnosis of anal diseases
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