Cervical cancer is one among the trivial forms of cancer that counts for 6.6% of all females cancers with an estimated 570,000 new cases in 2018. The mortality rate due to cervical cancer is approximately 90% in low or middle income countries due to lack of suitable pre-screening procedures and experienced medical staff. Colposcopy images or cervigrams, are the images that capture the cervical region, are considered as the gold standard by the medical experts for the identification and evaluation of cervical cancer. The visual assessment of cervigrams for recognizing cancer suffers from high inter- or intra-variations especially among less or unskilled medical experts. However, this method is dependent on colposcopists’ observation and it is more time consuming, tedious and laborious task which calls for development of computer-aided method for diagnosis of cervical cancer. With the technological advancements, deep learning has been commonly employed for providing automated solutions for disease diagnosis due to its self-learning capability. This paper presents a deep-learning-based method for cervix cancer classification using colposcopy images. The architecture of the proposed method namely, ColpoNet, has been motivated by the DenseNet model because it is computationally more efficient as compared to other models. Further, the method has been tested and validated on the dataset released by the National Cancer Institute and it has been compared with other deep-learning models namely AlexNet, VGG16, ResNet50, LeNet and GoogleNet to check scope of its applicability. The experimental analysis revealed that ColpoNet achieved an accuracy of 81.353% and shows the highest performance rate as compared to other state-of-the-art deep techniques. Such classification system can be deployed in clinics to enhance the early detection of cervical cancer in less developed countries.

中文翻译：

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ColpoNet用于使用阴道镜图像自动筛查宫颈癌

子宫颈癌是癌症的一种，占所有女性癌症的6.6％，2018年估计有570,000例新病例。由于缺乏适当的治疗方法，在中低收入国家中，子宫颈癌的死亡率约为90％预先检查程序和经验丰富的医务人员。阴道镜检查图像或子宫颈图是捕获宫颈区域的图像，医学专家认为这是鉴定和评估宫颈癌的金标准。用于识别癌症的子宫颈图的视觉评估存在较大的内部或内部变异，尤其是在技能熟练的专家中。但是，此方法取决于上校人员的观察，而且比较耗时，繁琐而费力的任务，需要开发用于诊断宫颈癌的计算机辅助方法。随着技术的进步，由于其具有自学习能力，深度学习已普遍用于提供疾病诊断的自动化解决方案。本文提出了一种基于深度学习的阴道镜图像宫颈癌分类方法。提议的方法的架构ColpoNet受到DenseNet模型的推动，因为与其他模型相比，该方法在计算上更加高效。此外，该方法已在美国国家癌症研究所（National Cancer Institute）发布的数据集中进行了测试和验证，并且已与AlexNet，VGG16，ResNet50，LeNet和GoogleNet等其他深度学习模型进行了比较，以检查其适用范围。实验分析表明，与其他最新的深度技术相比，ColpoNet的准确度达到了81.353％，并且显示出最高的性能。这种分类系统可以部署在诊所中，以提高欠发达国家中子宫颈癌的早期发现率。