MERAL Myanmar Education Research and Learning Portal
Item
{"_buckets": {"deposit": "fa8513fa-70fb-4c1b-aabc-1e473cbb603e"}, "_deposit": {"created_by": 73, "id": "7645", "owner": "73", "owners": [73], "owners_ext": {"displayname": "", "username": ""}, "pid": {"revision_id": 0, "type": "depid", "value": "7645"}, "status": "published"}, "_oai": {"id": "oai:meral.edu.mm:recid/00007645", "sets": ["user-miit"]}, "communities": ["miit"], "control_number": "7645", "item_1583103067471": {"attribute_name": "Title", "attribute_value_mlt": [{"subitem_1551255647225": "Detection and Classification of Lung Cancer Stages using Image Processing Techniques", "subitem_1551255648112": "en"}]}, "item_1583103085720": {"attribute_name": "Description", "attribute_value_mlt": [{"interim": "In current days, image processing techniques are\nwidely used in many medical areas for improving earlier detection and treatment stages, especially in various cancer nodules such as the lung cancer, breast cancer, brain cancer and so on. This paper shows the detection and classification of lung cancer stages based on CT Scan Images. The median filter algorithm is used for image processing. In this paper, morphological operations are used to detect lung cancer nodule. And then, extracts lowlevel\nfeatures from the detected nodule. This paper uses seven features area, perimeter, eccentricity and four texture features using Gray-level Co-occurrence Matrix (GLCM). Finally, the extracted features from the detected regions are\ngiven as input to 3-layer Artificial Neural Network (ANN) classifier to classify the detected lung cancer nodule into stages. Diagnosis is mostly based on CT (computed\ntomography) images. The lung cancer CT scan images for each stage obtain from the internet."}]}, "item_1583103108160": {"attribute_name": "Keywords", "attribute_value_mlt": [{"interim": "Preprocessing, Morphological Operations, Gray Level Co-occurrence Matrix, Artificial Neural Network."}]}, "item_1583103120197": {"attribute_name": "Files", "attribute_type": "file", "attribute_value_mlt": [{"accessrole": "open_access", "date": [{"dateType": "Available", "dateValue": "2021-01-20"}], "displaytype": "preview", "download_preview_message": "", "file_order": 0, "filename": "Detection and Classification of Lung Cancer Stages using Image Processing Techniques.pdf", "filesize": [{"value": "426 KB"}], "format": "application/pdf", "future_date_message": "", "is_thumbnail": false, "licensetype": "license_3", "mimetype": "application/pdf", "size": 426000.0, "url": {"url": "https://meral.edu.mm/record/7645/files/Detection and Classification of Lung Cancer Stages using Image Processing Techniques.pdf"}, "version_id": "68aa8953-5ac1-44f7-a291-1aae4b6cbbf9"}]}, "item_1583103131163": {"attribute_name": "Journal articles", "attribute_value_mlt": [{"subitem_issue": "Issue 4", "subitem_journal_title": "International Journal of Electrical Electronics \u0026 Computer Science Engineering", "subitem_pages": "Page-6", "subitem_volume": "Volume 6"}]}, "item_1583105942107": {"attribute_name": "Authors", "attribute_value_mlt": [{"subitem_authors": [{"subitem_authors_fullname": "Nwe Ni Kyaw"}, {"subitem_authors_fullname": "Kyaw Kyaw Naing"}, {"subitem_authors_fullname": "Phyu Myo Thwe"}, {"subitem_authors_fullname": "Kyawt Kyawt Htay"}, {"subitem_authors_fullname": "Hanni Htun"}]}]}, "item_1583108359239": {"attribute_name": "Upload type", "attribute_value_mlt": [{"interim": "Publication"}]}, "item_1583108428133": {"attribute_name": "Publication type", "attribute_value_mlt": [{"interim": "Journal article"}]}, "item_1583159729339": {"attribute_name": "Publication date", "attribute_value": "2019-08-20"}, "item_title": "Detection and Classification of Lung Cancer Stages using Image Processing Techniques", "item_type_id": "21", "owner": "73", "path": ["1582963674932", "1597396989070"], "permalink_uri": "http://hdl.handle.net/20.500.12678/0000007645", "pubdate": {"attribute_name": "Deposited date", "attribute_value": "2019-08-20"}, "publish_date": "2019-08-20", "publish_status": "0", "recid": "7645", "relation": {}, "relation_version_is_last": true, "title": ["Detection and Classification of Lung Cancer Stages using Image Processing Techniques"], "weko_shared_id": -1}
Detection and Classification of Lung Cancer Stages using Image Processing Techniques
http://hdl.handle.net/20.500.12678/0000007645
http://hdl.handle.net/20.500.12678/00000076452cfbde23-39d6-4a42-9f31-7f842d4d84b0
fa8513fa-70fb-4c1b-aabc-1e473cbb603e
Name / File | License | Actions |
---|---|---|
Detection and Classification of Lung Cancer Stages using Image Processing Techniques.pdf (426 KB)
|
Publication type | ||||||
---|---|---|---|---|---|---|
Journal article | ||||||
Upload type | ||||||
Publication | ||||||
Title | ||||||
Title | Detection and Classification of Lung Cancer Stages using Image Processing Techniques | |||||
Language | en | |||||
Publication date | 2019-08-20 | |||||
Authors | ||||||
Nwe Ni Kyaw | ||||||
Kyaw Kyaw Naing | ||||||
Phyu Myo Thwe | ||||||
Kyawt Kyawt Htay | ||||||
Hanni Htun | ||||||
Description | ||||||
In current days, image processing techniques are widely used in many medical areas for improving earlier detection and treatment stages, especially in various cancer nodules such as the lung cancer, breast cancer, brain cancer and so on. This paper shows the detection and classification of lung cancer stages based on CT Scan Images. The median filter algorithm is used for image processing. In this paper, morphological operations are used to detect lung cancer nodule. And then, extracts lowlevel features from the detected nodule. This paper uses seven features area, perimeter, eccentricity and four texture features using Gray-level Co-occurrence Matrix (GLCM). Finally, the extracted features from the detected regions are given as input to 3-layer Artificial Neural Network (ANN) classifier to classify the detected lung cancer nodule into stages. Diagnosis is mostly based on CT (computed tomography) images. The lung cancer CT scan images for each stage obtain from the internet. |
||||||
Keywords | ||||||
Preprocessing, Morphological Operations, Gray Level Co-occurrence Matrix, Artificial Neural Network. | ||||||
Journal articles | ||||||
Issue 4 | ||||||
International Journal of Electrical Electronics & Computer Science Engineering | ||||||
Page-6 | ||||||
Volume 6 |