Abstract
INTRODUCTION: Cervical cancer is the leading cancer among the other female cancers. It develops in the cervix of women. It takes decades in development thus can be preventable if diagnosed at an early stage. The cervix is classified into three types Type I/II/III. The efficacy of the treatment depends on the diagnosis of the right type of cervix. There is a thin line difference between the three types. Thus, identification of the right type of cervix becomes a difficult task for the health care providers too. To aid this problem, we proposed an algorithm based on the standard transfer learning approach used for building a model that classifies cervix images.OBJECTIVES: The objective of this study is to develop a cervical cancer predictive model based on deep learning and transfer learning techniques that will recognize and classify the cervix images into one of the classes (Type 1/Type2/Type3).METHODS: Techniques used for carrying out the experimental work includes deep learning and Transfer Learning. The three pertained models namely InceptionV3, ResNet50, and VGG19 are used for creating ConvNet that will classify the cervix images.RESULTS: The result of the experiment reveals that the Inception v3 model is performing better than Vgg19 and ResNet50 with an accuracy of 96.1% on the cervical cancer dataset.CONCLUSION: In the future, augmentation techniques can be employed to achieve better accuracy.
Highlights
Cervical cancer is the leading cancer among the other female cancers
We present a novel deep learning-based predictive model that diagnoses the stage of cervical cancer by classifying the cervix images into one of the three classes (Type 1/Type2/Type3)
Guo et al [33] worked on 30,000 smartphone-captured images and used ensembles deep learning to classify images into the cervix or non-cervix which achieve an accuracy of 91.6%. It can be observed from the above analysis that our proposed model performed fairly well in comparison with other existing methodologies as it achieves the accuracy of 97.1%
Summary
Cervical cancer is the leading cancer among the other female cancers. It develops in the cervix of women. Identification of the right type of cervix becomes a difficult task for the health care providers too To aid this problem, we proposed an algorithm based on the standard transfer learning approach used for building a model that classifies cervix images. OBJECTIVES: The objective of this study is to develop a cervical cancer predictive model based on deep learning and transfer learning techniques that will recognize and classify the cervix images into one of the classes (Type 1/Type2/Type). To aid health care providers, we proposed an algorithm using deep learning and transfer learning that will classify type the cervix images into 3 classes. We present a novel deep learning-based predictive model that diagnoses the stage of cervical cancer by classifying the cervix images into one of the three classes (Type 1/Type2/Type). Usage of the predictive model will help the healthcare providers in giving timely and cost-effective results
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