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Cervical cancer is one of the most well-known illnesses that afflict women worldwide, impacting over 6,000,000,000 people. It is now evident that the human papillomavirus (HPV) causes abnormal cells to appear in the cervix area, which is the sickness that leads to cervical cancer. It is the major attributor to mortality associated with the cancer. If it is the most common cause of mortality from cancer. It is also the most common cancer in men. It is much less fatal if detected early; if not, bleak results are guaranteed. Three classifications of cervical structure exist: These are also known as Type I, II, and III. Thus, the right type of cervix, at which the medication can be effective, must be distinguished. The line of distinction between the three types of images is very skinny. Choosing the right type of cervix is therefore not an easy thing to do. This makes the selection of the right type of cervix a very challenging one. With this information, a predictive model for cervical cancer has been developed to identify and categorize cervix images, using deep learning and transfer learning techniques. The Deep Learning CNN approach is the method used in this model to carry out the experimental work. This research used pre-trained models such as Dense Net, ResNet, VGG-16 and VGG-19. This research work utilized the Kaggle dataset for cervical cancer, the accuracy of the suggested model beat another transfer-learning model by 98.5%.

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A Novel CNN-Based Approach for Cervix Cancer Type Classification

  • 会议论文

  • 发表日期：

    2025年

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  • Shruti Mathur,
  • Akhilesh Kumar Sharma,
  • Anju Yadav,
  • Brij Kishore Sharma

摘要

Cervical cancer is one of the most well-known illnesses that afflict women worldwide, impacting over 6,000,000,000 people. It is now evident that the human papillomavirus (HPV) causes abnormal cells to appear in the cervix area, which is the sickness that leads to cervical cancer. It is the major attributor to mortality associated with the cancer. If it is the most common cause of mortality from cancer. It is also the most common cancer in men. It is much less fatal if detected early; if not, bleak results are guaranteed. Three classifications of cervical structure exist: These are also known as Type I, II, and III. Thus, the right type of cervix, at which the medication can be effective, must be distinguished. The line of distinction between the three types of images is very skinny. Choosing the right type of cervix is therefore not an easy thing to do. This makes the selection of the right type of cervix a very challenging one. With this information, a predictive model for cervical cancer has been developed to identify and categorize cervix images, using deep learning and transfer learning techniques. The Deep Learning CNN approach is the method used in this model to carry out the experimental work. This research used pre-trained models such as Dense Net, ResNet, VGG-16 and VGG-19. This research work utilized the Kaggle dataset for cervical cancer, the accuracy of the suggested model beat another transfer-learning model by 98.5%.