Design of a Honey Badger Optimization Algorithm with a Deep Transfer Learning-Based Osteosarcoma Classification Model
Osteosarcoma is one of the aggressive bone tumors with numerous histologic patterns. Histopathological inspection is a crucial criterion in the medical diagnosis of Osteosarcoma. Due to the advancement of computing power and hardware technology, pathological image analysis system based on artificial...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-12-01
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| Series: | Cancers |
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| Online Access: | https://www.mdpi.com/2072-6694/14/24/6066 |
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| author | Thavavel Vaiyapuri Akshya Jothi Kanagaraj Narayanasamy Kartheeban Kamatchi Seifedine Kadry Jungeun Kim |
| author_facet | Thavavel Vaiyapuri Akshya Jothi Kanagaraj Narayanasamy Kartheeban Kamatchi Seifedine Kadry Jungeun Kim |
| author_sort | Thavavel Vaiyapuri |
| collection | DOAJ |
| description | Osteosarcoma is one of the aggressive bone tumors with numerous histologic patterns. Histopathological inspection is a crucial criterion in the medical diagnosis of Osteosarcoma. Due to the advancement of computing power and hardware technology, pathological image analysis system based on artificial intelligence (AI) were more commonly used. But classifying many intricate pathology images by hand will be challenging for pathologists. The lack of labeling data makes the system difficult to build and costly. This article designs a Honey Badger Optimization with Deep Learning based Automated Osteosarcoma Classification (HBODL-AOC) model. The HBODL-AOC technique’s goal is to identify osteosarcoma’s existence using medical images. In the presented HBODL-AOC technique, image preprocessing is initially performed by contrast enhancement technique. For feature extraction, the HBODL-AOC technique employs a deep convolutional neural network-based Mobile networks (MobileNet) model with an Adam optimizer for hyperparameter tuning. Finally, the adaptive neuro-fuzzy inference system (ANFIS) approach is implemented for the HBO (Honey Badger Optimization) algorithm can tune osteosarcoma classification and the membership function (MF). To demonstrate the enhanced classification performance of the HBODL-AOC approach, a sequence of simulations was performed. The extensive simulation analysis portrayed the improved performance of the HBODL-AOC technique over existing DL models. |
| first_indexed | 2024-03-09T17:14:21Z |
| format | Article |
| id | doaj.art-65fb1f1aa4a8494f887fb93e41b853bc |
| institution | Directory Open Access Journal |
| issn | 2072-6694 |
| language | English |
| last_indexed | 2024-03-09T17:14:21Z |
| publishDate | 2022-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Cancers |
| spelling | doaj.art-65fb1f1aa4a8494f887fb93e41b853bc2023-11-24T13:45:36ZengMDPI AGCancers2072-66942022-12-011424606610.3390/cancers14246066Design of a Honey Badger Optimization Algorithm with a Deep Transfer Learning-Based Osteosarcoma Classification ModelThavavel Vaiyapuri0Akshya Jothi1Kanagaraj Narayanasamy2Kartheeban Kamatchi3Seifedine Kadry4Jungeun Kim5Department of Computer Science, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al Kharj 16278, Saudi ArabiaDepartment of Computational Intelligence, SRM Institute of Science and Technology, Kancheepuram 603203, IndiaDepartment of Computer Science, Karpagam Academy of Higher Education, Coimbatore 641021, IndiaDepartment of Computer Science and Engineering, Kalasalingam Academy of Research and Education, Krishnankoil 626126, IndiaDepartment of Applied Data Science, Noroff University College, 4612 Kristiansand, NorwayDepartment of Software, Kongju National University, Cheonan 31080, Republic of KoreaOsteosarcoma is one of the aggressive bone tumors with numerous histologic patterns. Histopathological inspection is a crucial criterion in the medical diagnosis of Osteosarcoma. Due to the advancement of computing power and hardware technology, pathological image analysis system based on artificial intelligence (AI) were more commonly used. But classifying many intricate pathology images by hand will be challenging for pathologists. The lack of labeling data makes the system difficult to build and costly. This article designs a Honey Badger Optimization with Deep Learning based Automated Osteosarcoma Classification (HBODL-AOC) model. The HBODL-AOC technique’s goal is to identify osteosarcoma’s existence using medical images. In the presented HBODL-AOC technique, image preprocessing is initially performed by contrast enhancement technique. For feature extraction, the HBODL-AOC technique employs a deep convolutional neural network-based Mobile networks (MobileNet) model with an Adam optimizer for hyperparameter tuning. Finally, the adaptive neuro-fuzzy inference system (ANFIS) approach is implemented for the HBO (Honey Badger Optimization) algorithm can tune osteosarcoma classification and the membership function (MF). To demonstrate the enhanced classification performance of the HBODL-AOC approach, a sequence of simulations was performed. The extensive simulation analysis portrayed the improved performance of the HBODL-AOC technique over existing DL models.https://www.mdpi.com/2072-6694/14/24/6066deep learningmetaheuristicshoney badger algorithmosteosarcoma classificationmedical imaging |
| spellingShingle | Thavavel Vaiyapuri Akshya Jothi Kanagaraj Narayanasamy Kartheeban Kamatchi Seifedine Kadry Jungeun Kim Design of a Honey Badger Optimization Algorithm with a Deep Transfer Learning-Based Osteosarcoma Classification Model Cancers deep learning metaheuristics honey badger algorithm osteosarcoma classification medical imaging |
| title | Design of a Honey Badger Optimization Algorithm with a Deep Transfer Learning-Based Osteosarcoma Classification Model |
| title_full | Design of a Honey Badger Optimization Algorithm with a Deep Transfer Learning-Based Osteosarcoma Classification Model |
| title_fullStr | Design of a Honey Badger Optimization Algorithm with a Deep Transfer Learning-Based Osteosarcoma Classification Model |
| title_full_unstemmed | Design of a Honey Badger Optimization Algorithm with a Deep Transfer Learning-Based Osteosarcoma Classification Model |
| title_short | Design of a Honey Badger Optimization Algorithm with a Deep Transfer Learning-Based Osteosarcoma Classification Model |
| title_sort | design of a honey badger optimization algorithm with a deep transfer learning based osteosarcoma classification model |
| topic | deep learning metaheuristics honey badger algorithm osteosarcoma classification medical imaging |
| url | https://www.mdpi.com/2072-6694/14/24/6066 |
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