CLASSIFICATION OF CERVICAL INTRAEPITHELIAL NEOPLASIA BASED ON MACHINE LEARNING AND DEEP LEARNING USING COLPOSCOPY IMAGES
Cervical lesions are one of the early signs for diagnosing cervical cancer, generally divided into three categories: Cervical Intraepithelial Neoplasia (CIN)1, CIN2, and CIN3. This study aims to develop a CIN classification model based on machine learning and deep learning. The machine learning m...
Saved in:
| Main Author: | |
|---|---|
| Format: | Theses |
| Language: | Indonesia |
| Online Access: | https://digilib.itb.ac.id/gdl/view/86901 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Institution: | Institut Teknologi Bandung |
| Language: | Indonesia |
| Summary: | Cervical lesions are one of the early signs for diagnosing cervical cancer, generally
divided into three categories: Cervical Intraepithelial Neoplasia (CIN)1, CIN2, and
CIN3. This study aims to develop a CIN classification model based on machine
learning and deep learning. The machine learning model is built by utilizing the
extraction values of texture and color features in cervical lesion images, namely
Gray Level Co-occurence Matrix (GLCM), L*a*b* color space, and Local Binary
Pattern (LBP). In contrast, the deep learning model is built by utilizing the basic
model Convolutional Neural Network (CNN). CIN images in training and testing
data use Intel Mobile Optical Detection Technologies, which is a collection of CIN
images tha have been grouped and verified by experts. The experiment in this
study was conducted to analyze the performance of the Extra Tree model of 0.98,
0.97, and 0.98. In contrast, the performance of the basic CNN model obtained
accuracy, sensitivity, and specificity values of 0.94, 0.96, and 0.89. The findings in
this study indicate that the dataset and conduct further testing with more complex
classification methods in order to improve the performance of the classification
model. |
|---|