An Automatic Method for Identification of Cervical Cancer based on Multilayer Perceptron Neural Network

Meenakshisundaram, N.; Ramkumar, G.

doi:10.1109/iceconf57129.2023.10083694

Cited by 3 publications

(1 citation statement)

References 11 publications

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“…Still on the topic of using deep-learning-based approaches to detect and classify cervical lesions, several recent works proved its feasibility to support cervical cancer screening, with proposed approaches that explored the usage of different deep convolutional neural networks ( [13,14]) and architectures, such as MobileNet [15,16], EfficientNet [15,17], as well as newly proposed networks, like the series-parallel fusion network (SPFNet) [18], Cervical Ensemble Network (CEENET) [19], or EfficientNet Fuzzy Extreme-Learning Machine (EN-FELM) [20]. Despite the promising results of these previous works, it should be noted that the vast majority do not take into account limitations like restricted computational resources to run the models.…”

Section: Related Workmentioning

confidence: 99%

Improving Mobile-Based Cervical Cytology Screening: A Deep Learning Nucleus-Based Approach for Lesion Detection

Mosiichuk,

Sampaio,

Viana

et al. 2023

Applied Sciences

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Liquid-based cytology (LBC) plays a crucial role in the effective early detection of cervical cancer, contributing to substantially decreasing mortality rates. However, the visual examination of microscopic slides is a challenging, time-consuming, and ambiguous task. Shortages of specialized staff and equipment are increasing the interest in developing artificial intelligence (AI)-powered portable solutions to support screening programs. This paper presents a novel approach based on a RetinaNet model with a ResNet50 backbone to detect the nuclei of cervical lesions on mobile-acquired microscopic images of cytology samples, stratifying the lesions according to The Bethesda System (TBS) guidelines. This work was supported by a new dataset of images from LBC samples digitalized with a portable smartphone-based microscope, encompassing nucleus annotations of 31,698 normal squamous cells and 1395 lesions. Several experiments were conducted to optimize the model’s detection performance, namely hyperparameter tuning, transfer learning, detected class adjustments, and per-class score threshold optimization. The proposed nucleus-based methodology improved the best baseline reported in the literature for detecting cervical lesions on microscopic images exclusively acquired with mobile devices coupled to the µSmartScope prototype, with per-class average precision, recall, and F1 scores up to 17.6%, 22.9%, and 16.0%, respectively. Performance improvements were obtained by transferring knowledge from networks pre-trained on a smaller dataset closer to the target application domain, as well as including normal squamous nuclei as a class detected by the model. Per-class tuning of the score threshold also allowed us to obtain a model more suitable to support screening procedures, achieving F1 score improvements in most TBS classes. While further improvements are still required to use the proposed approach in a clinical context, this work reinforces the potential of using AI-powered mobile-based solutions to support cervical cancer screening. Such solutions can significantly impact screening programs worldwide, particularly in areas with limited access and restricted healthcare resources.

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