DETECTION OF KERATOCONUS DISEASE DEPENDING ON CORNEAL TOPOGRAPHY USING DEEP LEARNING
Keratoconus is a disease that ML has contributed much in its diagnosis and management. It is not a widely prevalent disease, with a research gap caused by the absence of standardized datasets for model training and evaluation. This work presents a novel dataset, which strengthens the CNN model'...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Faculty of Engineering, University of Kufa
2025-02-01
|
| Series: | Mağallaẗ Al-kūfaẗ Al-handasiyyaẗ |
| Subjects: | |
| Online Access: | https://journal.uokufa.edu.iq/index.php/kje/article/view/17229 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832087031054860288 |
|---|---|
| author | Aseel Abdulhasan Hashim Mahdi Mazinani |
| author_facet | Aseel Abdulhasan Hashim Mahdi Mazinani |
| author_sort | Aseel Abdulhasan Hashim |
| collection | DOAJ |
| description | Keratoconus is a disease that ML has contributed much in its diagnosis and management. It is not a widely prevalent disease, with a research gap caused by the absence of standardized datasets for model training and evaluation. This work presents a novel dataset, which strengthens the CNN model's resilience and creates standards for assessing keratoconus diagnostic techniques. The research depends on data of patients examined at Jenna Ophthalmic Center in Baghdad. The proposed system works on three stages: pre-processing, feature extraction, and classification with machine learning algorithms including NB, KNN, ADA, DT, and CNN deep learning. The pre-processing stage involves cropping images to retain the relevant maps, which were subjected to contrast enhancement to improve image quality. The pre-processed data is then fed into Machine Learning(ML) algorithms and Convolutional Neural Network(CNN) models, by which the four corneal maps were analyzed. The precision of the ML method was quantified, yielding a precision score of 0.79 for the AdaBoost algorithm and an impressive score of 0.99 for the suggested CNN model exemplifying its high accuracy and ability to surpass all machine learning approaches. Applying PCA for feature extraction before utilizing tradition ML algorithms and CNN helps in achieving high-accuracy results.
|
| format | Article |
| id | doaj-art-84f3912da0b94495b2e74a68c0506ac9 |
| institution | Kabale University |
| issn | 2071-5528 2523-0018 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Faculty of Engineering, University of Kufa |
| record_format | Article |
| series | Mağallaẗ Al-kūfaẗ Al-handasiyyaẗ |
| spelling | doaj-art-84f3912da0b94495b2e74a68c0506ac92025-02-06T08:07:28ZengFaculty of Engineering, University of KufaMağallaẗ Al-kūfaẗ Al-handasiyyaẗ2071-55282523-00182025-02-01160146347810.30572/2018/KJE/160125DETECTION OF KERATOCONUS DISEASE DEPENDING ON CORNEAL TOPOGRAPHY USING DEEP LEARNINGAseel Abdulhasan Hashim0https://orcid.org/0009-0003-5393-8780Mahdi Mazinani1Department of Computer Science, College of Education for Pure Science Ibn Al-Haitham, University of Baghdad, IraqDepartment of Electrical and Electronic Engineering, Shahr-e-Qods Branch, Islamic Azad University, Tehran, IranKeratoconus is a disease that ML has contributed much in its diagnosis and management. It is not a widely prevalent disease, with a research gap caused by the absence of standardized datasets for model training and evaluation. This work presents a novel dataset, which strengthens the CNN model's resilience and creates standards for assessing keratoconus diagnostic techniques. The research depends on data of patients examined at Jenna Ophthalmic Center in Baghdad. The proposed system works on three stages: pre-processing, feature extraction, and classification with machine learning algorithms including NB, KNN, ADA, DT, and CNN deep learning. The pre-processing stage involves cropping images to retain the relevant maps, which were subjected to contrast enhancement to improve image quality. The pre-processed data is then fed into Machine Learning(ML) algorithms and Convolutional Neural Network(CNN) models, by which the four corneal maps were analyzed. The precision of the ML method was quantified, yielding a precision score of 0.79 for the AdaBoost algorithm and an impressive score of 0.99 for the suggested CNN model exemplifying its high accuracy and ability to surpass all machine learning approaches. Applying PCA for feature extraction before utilizing tradition ML algorithms and CNN helps in achieving high-accuracy results. https://journal.uokufa.edu.iq/index.php/kje/article/view/17229keratoconuscorneal disorderirregular astigmatismconvolutional neural network (cnn)precisionophthalmic center |
| spellingShingle | Aseel Abdulhasan Hashim Mahdi Mazinani DETECTION OF KERATOCONUS DISEASE DEPENDING ON CORNEAL TOPOGRAPHY USING DEEP LEARNING Mağallaẗ Al-kūfaẗ Al-handasiyyaẗ keratoconus corneal disorder irregular astigmatism convolutional neural network (cnn) precision ophthalmic center |
| title | DETECTION OF KERATOCONUS DISEASE DEPENDING ON CORNEAL TOPOGRAPHY USING DEEP LEARNING |
| title_full | DETECTION OF KERATOCONUS DISEASE DEPENDING ON CORNEAL TOPOGRAPHY USING DEEP LEARNING |
| title_fullStr | DETECTION OF KERATOCONUS DISEASE DEPENDING ON CORNEAL TOPOGRAPHY USING DEEP LEARNING |
| title_full_unstemmed | DETECTION OF KERATOCONUS DISEASE DEPENDING ON CORNEAL TOPOGRAPHY USING DEEP LEARNING |
| title_short | DETECTION OF KERATOCONUS DISEASE DEPENDING ON CORNEAL TOPOGRAPHY USING DEEP LEARNING |
| title_sort | detection of keratoconus disease depending on corneal topography using deep learning |
| topic | keratoconus corneal disorder irregular astigmatism convolutional neural network (cnn) precision ophthalmic center |
| url | https://journal.uokufa.edu.iq/index.php/kje/article/view/17229 |
| work_keys_str_mv | AT aseelabdulhasanhashim detectionofkeratoconusdiseasedependingoncornealtopographyusingdeeplearning AT mahdimazinani detectionofkeratoconusdiseasedependingoncornealtopographyusingdeeplearning |