An Improved Image Classification Method for Cervical Precancerous Lesions Based on ShuffleNet

Abstract

With the rapid development of deep learning, automatic lesion detection is used widely in clinical screening. To solve the problem that existing deep learning-based cervical precancerous lesion detection algorithms cannot meet high classification accuracy and fast running speed at the same time, a ShuffleNet-based cervical precancerous lesion classification method is proposed. By adding channel attention to the ShuffleNet, the network performance is improved. In this study, the image dataset is classified into five categories: normal, cervical cancer, LSIL (CIN1), HSIL (CIN2/CIN3), and cervical neoplasm. The colposcopy images are expanded to solve the problems of the lack of colposcopy images and the uneven distribution of images from each category. For the test dataset, the accuracy of the proposed CNN models is 81.23% and 81.38%. Our classifier achieved an AUC score of 0.99. The experimental results show that the colposcopy image classification network based on artificial intelligence has good performance in classification accuracy and model size, and it has high clinical applicability.

Revolutionizing Women’s Health: A Comprehensive Review of Artificial Intelligence Advancements in Gynecology

“…The authors proved the synergetic benefits of the ensembled model, presenting a higher AUC of 0.921. A ShuffleNet-based cervical precancerous lesion classification method based on colposcopy images was developed by Fang and colleagues [38]. The image dataset was classified into five categories, namely normal, cervical cancer, LSILs (CIN1), HSILs (CIN2/CIN3), and cervical neoplasm.…”

FMA-Net: Fusion of Multi-Scale Attention for Grading Cervical Precancerous Lesions

“…Woo et al proposed the CBAM module [21], which combines the channel attention mechanism and spatial attention mechanism to capture both channel correlations and spatial correlations in images, thereby enhancing the model's performance. Fang et al [22] proposed a deep reverse residual network for CIN grading based on the improved channel attention using ShuffleNet V1 [23]. Although the current attention mechanism enhances weights, it typically performs weight calculation learning only in a single dimension and is limited to learning single-scale features in one pass, lacking information exchange across multiple dimensions and scales.…”

Deep learning-based cervical lesion segmentation in colposcopic images

“…While developed nations exhibit a decreasing trend in cervical cancer incidence, developing and underdeveloped countries are witnessing a surge in the occurrence of cervical cancer [2], [3]. Colposcopy is a procedure during which a solution called acetic acid is smeared on the cervix region and a sequence of images of the cervix are captured using the probe [4]. These images are called cervigrams [5].…”

“…However, these methods still encounter challenges in fully leveraging the spatial information inherent in images to attain segmentation results of the desired quality. Natural image processing has seen a revolution recently thanks to convolutional neural networks (CNNs), which take advantage of their hierarchically obtained highly representative features [4], [5], [10]. Medical image analysis has benefitted significantly by this development [15]- [17].…”