Simplified Convolutional Neural Network Application for Cervix Type Classification via Colposcopic Images

Abstract

The inner parts of the human body are usually inspected endoscopically using special equipment. For instance, each part of the female reproductive system can be examined endoscopically (laparoscopy, hysteroscopy, and colposcopy). The primary purpose of colposcopy is the early detection of malignant lesions of the cervix. Cervical cancer (CC) is one of the most common cancers in women worldwide, especially in middle- and low-income countries. Therefore, there is a growing demand for approaches that aim to detect precancerous lesions, ideally without quality loss. Despite its high efficiency, this method has some disadvantages, including subjectivity and pronounced dependence on the operator’s experience. The objective of the current work is to propose an alternative to overcoming these limitations by utilizing the neural network approach. The classifier is trained to recognize and classify lesions. The classifier has a high recognition accuracy and a low computational complexity. The classification accuracies for the classes normal, LSIL, HSIL, and suspicious for invasion were 95.46%, 79.78%, 94.16%, and 97.09%, respectively. We argue that the proposed architecture is simpler than those discussed in other articles due to the use of the global averaging level of the pool. Therefore, the classifier can be implemented on low-power computing platforms at a reasonable cost.

Transfer learning supported accurate assessment of multiclass cervix type images

“…The transformation region is detected from the cervix images by employing MobileNetv2-YOLOv3. Pavlov et al 18 developed a CNN classifier architecture to classify colposcopic cervix images as Normal, LSIL (Low-grade squamous intraepithelial lesion), HSIL (High-grade squamous intraepithelial lesion), and suspicious for invasion. The authors have given their views on the complexity and diversity of colposcopic images, and how heterogeneity varies according to a wide range of scales.…”