Design of a Honey Badger Optimization Algorithm with a Deep Transfer Learning-Based Osteosarcoma Classification Model.
Simple Summary: 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 bas...
| Publicado en: | Cancers Vol. 14; no. 24; pp. 6066 - 6083 |
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| Autores principales: | , , , , , |
| Formato: | algorithm equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
MDPI
12/15/2022
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=160958647&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 160958647 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 20726694 B74B jtl: Cancers issn: 20726694 maglogo: N pubinfo: dt: 12/15/2022 vid: 14 iid: 24 pid: 97109 pub: MDPI artinfo: ui: 160958647 160958647 160958647 10.3390/cancers14246066 160958647 ppf: 6066 ppct: 17 formats: tig: atl: Design of a Honey Badger Optimization Algorithm with a Deep Transfer Learning-Based Osteosarcoma Classification Model. aug: au: Vaiyapuri, Thavavel Jothi, Akshya Narayanasamy, Kanagaraj Kamatchi, Kartheeban Kadry, Seifedine Kim, Jungeun affil: Department of Computer Science, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al Kharj 16278, Saudi Arabia sug: subj: Algorithms Osteosarcoma Classification Theory Construction Models, Educational Deep Learning Osteosarcoma Diagnosis Diagnosis, Computer Assisted Neural Networks (Computer) ab: Simple Summary: 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 were more commonly used. Manual examination of the histopathological images is a difficult and laborious task. The lack of labeling data makes the system difficult to build and costly. Therefore, this study aims to develop an automated computer-aided diagnosis model for osteosarcoma classification. The proposed model uses deep learning, hyperparameter optimizer, and fuzzy logic for the classification process. 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. pubtype: Academic Journal doctype: algorithm equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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