Optimal Deep Stacked Sparse Autoencoder Based Osteosarcoma Detection and Classification Model

Osteosarcoma is a kind of bone cancer which generally starts to develop in the lengthy bones in the legs and arms. Because of an increase in occurrence of cancer and patient-specific treatment options, the detection and classification of cancer becomes a difficult process. The manual recognition of...

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Vydáno v:Healthcare (Basel) Ročník 10; číslo 6; s. 1040
Hlavní autoři: Fakieh, Bahjat, AL-Ghamdi, Abdullah S. AL-Malaise, Ragab, Mahmoud
Médium: Journal Article
Jazyk:angličtina
Vydáno: Switzerland MDPI AG 02.06.2022
MDPI
Témata:
Algorithms
Bone cancer
Classification
Histology
Medical diagnosis
Medical prognosis
Sarcoma
Semantics
Tumors
image processing
medical images
computer aided diagnosis
osteosarcoma
deep transfer learning
ISSN:2227-9032, 2227-9032
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Shrnutí:Osteosarcoma is a kind of bone cancer which generally starts to develop in the lengthy bones in the legs and arms. Because of an increase in occurrence of cancer and patient-specific treatment options, the detection and classification of cancer becomes a difficult process. The manual recognition of osteosarcoma necessitates expert knowledge and is time consuming. An earlier identification of osteosarcoma can reduce the death rate. With the development of new technologies, automated detection models can be exploited for medical image classification, thereby decreasing the expert’s reliance and resulting in timely identification. In recent times, an amount of Computer-Aided Detection (CAD) systems are available in the literature for the segmentation and detection of osteosarcoma using medicinal images. In this view, this research work develops a wind driven optimization with deep transfer learning enabled osteosarcoma detection and classification (WDODTL-ODC) method. The presented WDODTL-ODC model intends to determine the presence of osteosarcoma in the biomedical images. To accomplish this, the osteosarcoma model involves Gaussian filtering (GF) based on pre-processing and contrast enhancement techniques. In addition, deep transfer learning using a SqueezNet model is utilized as a featured extractor. At last, the Wind Driven Optimization (WDO) algorithm with a deep-stacked sparse auto-encoder (DSSAE) is employed for the classification process. The simulation outcome demonstrated that the WDODTL-ODC technique outperformed the existing models in the detection of osteosarcoma on biomedical images.
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ISSN:2227-9032
2227-9032
DOI:10.3390/healthcare10061040