Abstract
Cervical cancer is the fourth most prevalent disease in women. Accurate and timely cancer detection can save lives. Automatic and reliable cervical cancer detection methods can be devised through the accurate segmentation and classification of Pap smear cell images. This paper presents an approach to whole cervical cell segmentation using a mask regional convolutional neural network (Mask R-CNN) and classifies this using a smaller Visual Geometry Group-like Network (VGG-like Net). ResNet10 is used to make full use of spatial information and prior knowledge as the backbone of the Mask R-CNN. We evaluate our proposed method on the Herlev Pap Smear dataset. In the segmentation phase, when Mask R-CNN is applied on the whole cell, it outperforms the previous segmentation method in precision (0.92±0.06), recall (0.91±0.05) and ZSI (0.91±0.04). In the classification phase, VGG-like Net is applied on the whole segmented cell and yields a sensitivity score of more than 96% with low standard deviation (±2.8%) for the binary classification problem and yields a higher result of more than 95% with low standard deviation (maximum 4.2% in accuracy measurement) for the 7-class problem in terms of sensitivity, specificity, accuracy, h-mean, and F1 score.
Highlights
IntroductionGlobal cancer data reveals that cervical cancer is the fourth most prevalent disease among females, with an approximately 90% fatality rate in underdeveloped and developing nations due to the absence of public knowledge of its causes and impacts [1]
Cancer is a life-threatening disease and has become a major burden worldwide
We summarize the performance of our segmentation using precision, recall, a Zijdenbos similarity index (ZSI) and specificity, whereas the performance of the classification is evaluated using F1 score, accuracy, sensitivity, specificity, and h-mean
Summary
Global cancer data reveals that cervical cancer is the fourth most prevalent disease among females, with an approximately 90% fatality rate in underdeveloped and developing nations due to the absence of public knowledge of its causes and impacts [1]. This lethal disease can be detected by the regular Pap smear testing of the cervical cells. A manual visual examination is time consuming and the analysis and classification of hundreds or thousands of cells can be inaccurate due to human error. Due to size and shape variations of normal and abnormal cells, accurate cell segmentation and classification is crucial to differentiate between normal and abnormal cells
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