Prediction of Preliminary Tests for Cervical Cancer using Artificial Intelligence Models

被引:0
|
作者
Mohanraj, V [1 ]
Senthilkumar, J. [1 ]
Suresh, Y. [1 ]
Valaramathi, B. [2 ]
Sivanantham, S. [3 ]
机构
[1] Sona Coll Technol, Informat Technol, Salem, Tamil Nadu, India
[2] VIT, Sch CSE & IS, Sofware & Syst Engn, Vellore, Tamil Nadu, India
[3] SIMATS, Saveetha Sch Engn, CSE, Chennai, Tamil Nadu, India
来源
PRZEGLAD ELEKTROTECHNICZNY | 2024年 / 100卷 / 07期
关键词
Cervical Cancer; Deep Convolution Neural Network; Decision Tree Classifier; Accuracy; Precision;
D O I
10.15199/48.2024.07.19
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Nowadays, Aritificial Intellgience (AI) based models are extensively used in the medical science for early detection of choronic diseases. AI model plays a vital role in detecting cervical cancer in women at early stage. Cervical cancer is abnormal growth of cells in the cervix. Vagina is connected to uterus through the cervix. Mostly, various strains of Human papillomavirus (HPV) cause the infection over the cervix. A prolonged virus infection over cervix causes some cervical cells become cancer cells. It is difficult to dectect early sign of the cervical cancer. The proposed method explores cervical cancer detection and provides information on the necessary tests to be taken.The initial level of testing is achieved by getting information from users directly and processing it using a Decision Tree based classifier model. The classifier provide information on the mandatory tests that have to be taken. Then the secondary level of testing is carried out using Deep Convolution Neural Network model over a Colposcopy image of the cervix to identify the tumor region in the cervix. The model predicts the causes of cervical cancer based on the collected user information. The performance of the algorithm is evaluated based on Test accuracy, Recall, and precision. The highest cervical cancer prediction accuracy is achieved through AI model comprising Decision Tree and Deep Convolution Neural network model.
引用
收藏
页码:89 / 95
页数:7
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