Detection of Liver Tumour Using Deep Learning Based Segmentation with Coot Extreme Learning Model
Systems for medical analytics and decision making that make use of multimodal intelligence are of critical importance in the field of healthcare. Liver cancer is one of the most frequent types of cancer and early identification of it is crucial for effective therapy. Liver tumours share the same bri...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-03-01
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| Series: | Biomedicines |
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| Online Access: | https://www.mdpi.com/2227-9059/11/3/800 |
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| author | Kalaivani Sridhar Kavitha C Wen-Cheng Lai Balasubramanian Prabhu Kavin |
| author_facet | Kalaivani Sridhar Kavitha C Wen-Cheng Lai Balasubramanian Prabhu Kavin |
| author_sort | Kalaivani Sridhar |
| collection | DOAJ |
| description | Systems for medical analytics and decision making that make use of multimodal intelligence are of critical importance in the field of healthcare. Liver cancer is one of the most frequent types of cancer and early identification of it is crucial for effective therapy. Liver tumours share the same brightness and contrast characteristics as their surrounding tissues. Likewise, irregular tumour shapes are a serious concern that varies with cancer stage and tumour kind. There are two main phases of tumour segmentation in the liver: identifying the liver, and then segmenting the tumour itself. Conventional interactive segmentation approaches, however, necessitate a high number of intensity levels, whereas recently projected CNN-based interactive segmentation approaches are constrained by low presentation on liver tumour images. This research provides a unique deep Learning based Segmentation with Coot Extreme Learning Model approach that shows high efficiency in results and also detects tumours from the publicly available data of liver images. Specifically, the study processes the initial segmentation with a small number of additional users clicks to generate an improved segmentation by incorporating inner boundary points through the proposed geodesic distance encoding method. Finally, classification is carried out using an Extreme Learning Model, with the classifier’s parameters having been ideally chosen by means of the Coot Optimization algorithm (COA). On the 3D-IRCADb1 dataset, the research evaluates the segmentation quality metrics DICE and accuracy, finding improvements over approaches in together liver-coloured and tumour separation. |
| first_indexed | 2024-03-11T06:53:37Z |
| format | Article |
| id | doaj.art-7a63bded4a2d4c1bacfd47fc412c539f |
| institution | Directory Open Access Journal |
| issn | 2227-9059 |
| language | English |
| last_indexed | 2024-03-11T06:53:37Z |
| publishDate | 2023-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Biomedicines |
| spelling | doaj.art-7a63bded4a2d4c1bacfd47fc412c539f2023-11-17T09:45:47ZengMDPI AGBiomedicines2227-90592023-03-0111380010.3390/biomedicines11030800Detection of Liver Tumour Using Deep Learning Based Segmentation with Coot Extreme Learning ModelKalaivani Sridhar0Kavitha C1Wen-Cheng Lai2Balasubramanian Prabhu Kavin3Department of Computer Science, Bharathidasan University, Tiruchirappalli 620024, Tamil Nadu, IndiaDepartment of Computer Science and Engineering, Sathyabama Institute of Science and Technology, Chennai 600119, Tamil Nadu, IndiaBachelor Program in Industrial Projects, National Yunlin University of Science and Technology, Douliu 640301, TaiwanDepartment of Data Science and Business System, SRM Institute of Science and Technology, Kattankulathur Campus, Chengalpattu 603203, Tamil Nadu, IndiaSystems for medical analytics and decision making that make use of multimodal intelligence are of critical importance in the field of healthcare. Liver cancer is one of the most frequent types of cancer and early identification of it is crucial for effective therapy. Liver tumours share the same brightness and contrast characteristics as their surrounding tissues. Likewise, irregular tumour shapes are a serious concern that varies with cancer stage and tumour kind. There are two main phases of tumour segmentation in the liver: identifying the liver, and then segmenting the tumour itself. Conventional interactive segmentation approaches, however, necessitate a high number of intensity levels, whereas recently projected CNN-based interactive segmentation approaches are constrained by low presentation on liver tumour images. This research provides a unique deep Learning based Segmentation with Coot Extreme Learning Model approach that shows high efficiency in results and also detects tumours from the publicly available data of liver images. Specifically, the study processes the initial segmentation with a small number of additional users clicks to generate an improved segmentation by incorporating inner boundary points through the proposed geodesic distance encoding method. Finally, classification is carried out using an Extreme Learning Model, with the classifier’s parameters having been ideally chosen by means of the Coot Optimization algorithm (COA). On the 3D-IRCADb1 dataset, the research evaluates the segmentation quality metrics DICE and accuracy, finding improvements over approaches in together liver-coloured and tumour separation.https://www.mdpi.com/2227-9059/11/3/800tumour predictioncoot optimization algorithmextreme learning modeldeep learning-based interactive segmentationintensity levels |
| spellingShingle | Kalaivani Sridhar Kavitha C Wen-Cheng Lai Balasubramanian Prabhu Kavin Detection of Liver Tumour Using Deep Learning Based Segmentation with Coot Extreme Learning Model Biomedicines tumour prediction coot optimization algorithm extreme learning model deep learning-based interactive segmentation intensity levels |
| title | Detection of Liver Tumour Using Deep Learning Based Segmentation with Coot Extreme Learning Model |
| title_full | Detection of Liver Tumour Using Deep Learning Based Segmentation with Coot Extreme Learning Model |
| title_fullStr | Detection of Liver Tumour Using Deep Learning Based Segmentation with Coot Extreme Learning Model |
| title_full_unstemmed | Detection of Liver Tumour Using Deep Learning Based Segmentation with Coot Extreme Learning Model |
| title_short | Detection of Liver Tumour Using Deep Learning Based Segmentation with Coot Extreme Learning Model |
| title_sort | detection of liver tumour using deep learning based segmentation with coot extreme learning model |
| topic | tumour prediction coot optimization algorithm extreme learning model deep learning-based interactive segmentation intensity levels |
| url | https://www.mdpi.com/2227-9059/11/3/800 |
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