DEEP LEARNING BASED CERVICAL CANCER DISEASE DETECTION AND CLASSIFICATION MODEL
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- 1-5 Chapters
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- Recommended for : Student Researchers
- NGN 5000
Abstract
Cervical cancer is the second most common and second most deadly cancer in Ethiopia. The disease's incidence and prevalence are increasing over time due to population growth and aging, as well as an increase in the prevalence of well-established risk factors. Cervical cancer knowledge and awareness among Ethiopian women is quite low. It is the most deadly disease caused by the uncontrolled growth of body cells, accounting for approximately 9.6 million deaths each year in world. In women, abnormal cell growth can affect various body organs such as the breast and the cervix. 85-90 percent of the fatality rate of cervical cancer occur in low and middle-income countries due to a lack of public awareness about the disease's causes and consequences. As a result, it is necessary to create a cervical cancer detection and classification model using deep learning techniques to assist experts. Sample of cervical cancer images were taken from Bethazeta Hospital in Addis Ababa, Ethiopia and some of data was addedfrom public dataset. It is proposed to detect and classify cervical cancer using deep learning model. The proposed approach has two main phases. In first phase the designed model is trained and tested by collected dataset and the data is classified using different neural network. Finally, the deep learning model that can detect and classify the given image in to Type_1, Type_2 and Type_3 is done. The dataset contains 2085 original cervical cancer images. From this, 80% of the images are used for training and the rest for testing the model. During training, data augmentation technique is used to generate more images to fit the proposed model using by Keras libraries. The Convolutional Neural Network and Hybrid of Convolutional NeuralNetwork and Long Short Term Memory model can successfully detect and classify the given image with an accuracy of 99.04% and 98.72% respectively.
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