CERVICAL CANCER DETECTION AND CLASSIFICATION USING MRIS
Cervical Cancer (CC) is the second most frequent malignancy in women worldwide, with a 60 % mortality rate; it is the leading cause of death worldwide. The majority of cervical cancer deaths occur in less developed countries where there is a lack of screening programs and sensitization about the dis...
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| Format: | Article |
| Language: | English |
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Scientific Research Support Fund of Jordan (SRSF) and Princess Sumaya University for Technology (PSUT)
2022-06-01
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| Series: | Jordanian Journal of Computers and Information Technology |
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| Online Access: | http://www.ejmanager.com/fulltextpdf.php?mno=139511 |
| _version_ | 1818209639757512704 |
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| author | Ichrak Khoulqi Najlae Idrissi |
| author_facet | Ichrak Khoulqi Najlae Idrissi |
| author_sort | Ichrak Khoulqi |
| collection | DOAJ |
| description | Cervical Cancer (CC) is the second most frequent malignancy in women worldwide, with a 60 % mortality rate; it is the leading cause of death worldwide. The majority of cervical cancer deaths occur in less developed countries where there is a lack of screening programs and sensitization about the disease. Because CC cannot be detected in its early stages since it does not reveal any symptoms and a long latent period. Accurate staging can aid radiologists in providing effective therapy by utilizing diagnostic methods such as MRIs. In this paper, two approaches are proposed, the first consist of introducing an automatic system for early detection of CC using image processing techniques and axial, sagittal T2-weighted MRIs for analysis to determine the pathological stage of tumour and to identify the real impact of cancer that will help the patient to be treated with high efficiency and properly. This detection process goes through three major steps, i.e. Preprocessing to make the representation of MRIs significant and easy to be analyzed, then the Segmentation was performed by Region Growing and Geometric deformable techniques to extract the Region Of Interests (ROIs).In the next step, we extract two categories of features based on Statistical and Transform methods in order to describe our ROIs, at the final step, five classifiers were trained to classify the MRIs into two classes: Benign or Malign. The second approach aims to increase the performance of pretrained Deep Convolutional Neural Networks (DCNNs) based on Transfer Learning (TL) used to classify our Female Pelvis Dataset (FP_Dataset) by adopting the stacking generalized method that provides a more efficient and robust classifier. Data augmentation is a pre-processing method applied to our MRIs and a dropout layer is used to prevent Networks from overfitting in our small Dataset. The results of experiments show that data augmentation and stacking generalization are an efficient way to improve accuracy rate of classification. [JJCIT 2022; 8(2.000): 141-158] |
| first_indexed | 2024-12-12T05:03:55Z |
| format | Article |
| id | doaj.art-202754a9a2a645a4b8490d2103952bba |
| institution | Directory Open Access Journal |
| issn | 2413-9351 |
| language | English |
| last_indexed | 2024-12-12T05:03:55Z |
| publishDate | 2022-06-01 |
| publisher | Scientific Research Support Fund of Jordan (SRSF) and Princess Sumaya University for Technology (PSUT) |
| record_format | Article |
| series | Jordanian Journal of Computers and Information Technology |
| spelling | doaj.art-202754a9a2a645a4b8490d2103952bba2022-12-22T00:37:08ZengScientific Research Support Fund of Jordan (SRSF) and Princess Sumaya University for Technology (PSUT)Jordanian Journal of Computers and Information Technology2413-93512022-06-018214115810.5455/jjcit.71-1640595124139511CERVICAL CANCER DETECTION AND CLASSIFICATION USING MRISIchrak Khoulqi0Najlae Idrissi1Faculty of Sciences and Technics Beni Mellal,University Sultan Moulay Slimane, Morocco. Faculty of Sciences and Technics Beni Mellal,University Sultan Moulay Slimane, Morocco.Cervical Cancer (CC) is the second most frequent malignancy in women worldwide, with a 60 % mortality rate; it is the leading cause of death worldwide. The majority of cervical cancer deaths occur in less developed countries where there is a lack of screening programs and sensitization about the disease. Because CC cannot be detected in its early stages since it does not reveal any symptoms and a long latent period. Accurate staging can aid radiologists in providing effective therapy by utilizing diagnostic methods such as MRIs. In this paper, two approaches are proposed, the first consist of introducing an automatic system for early detection of CC using image processing techniques and axial, sagittal T2-weighted MRIs for analysis to determine the pathological stage of tumour and to identify the real impact of cancer that will help the patient to be treated with high efficiency and properly. This detection process goes through three major steps, i.e. Preprocessing to make the representation of MRIs significant and easy to be analyzed, then the Segmentation was performed by Region Growing and Geometric deformable techniques to extract the Region Of Interests (ROIs).In the next step, we extract two categories of features based on Statistical and Transform methods in order to describe our ROIs, at the final step, five classifiers were trained to classify the MRIs into two classes: Benign or Malign. The second approach aims to increase the performance of pretrained Deep Convolutional Neural Networks (DCNNs) based on Transfer Learning (TL) used to classify our Female Pelvis Dataset (FP_Dataset) by adopting the stacking generalized method that provides a more efficient and robust classifier. Data augmentation is a pre-processing method applied to our MRIs and a dropout layer is used to prevent Networks from overfitting in our small Dataset. The results of experiments show that data augmentation and stacking generalization are an efficient way to improve accuracy rate of classification. [JJCIT 2022; 8(2.000): 141-158]http://www.ejmanager.com/fulltextpdf.php?mno=139511cervical cancermrisegmentationfeaturesdcnnstransfer learningstackingclassification |
| spellingShingle | Ichrak Khoulqi Najlae Idrissi CERVICAL CANCER DETECTION AND CLASSIFICATION USING MRIS Jordanian Journal of Computers and Information Technology cervical cancer mri segmentation features dcnns transfer learning stacking classification |
| title | CERVICAL CANCER DETECTION AND CLASSIFICATION USING MRIS |
| title_full | CERVICAL CANCER DETECTION AND CLASSIFICATION USING MRIS |
| title_fullStr | CERVICAL CANCER DETECTION AND CLASSIFICATION USING MRIS |
| title_full_unstemmed | CERVICAL CANCER DETECTION AND CLASSIFICATION USING MRIS |
| title_short | CERVICAL CANCER DETECTION AND CLASSIFICATION USING MRIS |
| title_sort | cervical cancer detection and classification using mris |
| topic | cervical cancer mri segmentation features dcnns transfer learning stacking classification |
| url | http://www.ejmanager.com/fulltextpdf.php?mno=139511 |
| work_keys_str_mv | AT ichrakkhoulqi cervicalcancerdetectionandclassificationusingmris AT najlaeidrissi cervicalcancerdetectionandclassificationusingmris |