Effectiveness of Learning Systems from Common Image File Types to Detect Osteosarcoma Based on Convolutional Neural Networks (CNNs) Models

Loraksa, Chanunya; Mongkolsomlit, Sirima; Nimsuk, Nitikarn; Uscharapong, Meenut; Kiatisevi, Piya

doi:10.3390/jimaging8010002

Cited by 10 publications

(7 citation statements)

References 31 publications

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“…A detailed comparative analysis of the results offered by the WDODTL-ODC model with recent models is provided in Table 3 [ 24 , 25 ]. Figure 9 reports a brief

examination of the WDODTL-ODC model with existing models.…”

Section: Resultsmentioning

confidence: 99%

Optimal Deep Stacked Sparse Autoencoder Based Osteosarcoma Detection and Classification Model

2022

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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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“…A detailed comparative analysis of the results offered by the WDODTL-ODC model with recent models is provided in Table 3 [ 24 , 25 ]. Figure 9 reports a brief

examination of the WDODTL-ODC model with existing models.…”

Section: Resultsmentioning

confidence: 99%

Optimal Deep Stacked Sparse Autoencoder Based Osteosarcoma Detection and Classification Model

2022

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“…Loraksa et al 34 used ResNet‐50, VGG‐16, and MobileNet‐V2 convolutional neural network (CNN) to segment osteosarcoma and achieved good results in different directions. A novel CNN architecture, called C‐Net, was posted in the paper of Barzekar et al 35 C‐Net is composed of multiple network concatenation.…”

Section: Related Workmentioning

confidence: 99%

A medical assistant segmentation method for MRI images of osteosarcoma based on DecoupleSegNet

Guo

Gou

et al. 2022

Int J of Intelligent Sys

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Nowadays, the most common primary bone tumor is osteosarcoma, which mostly occurs in teenagers. A common diagnosis method is currently that doctors manually diagnose osteosarcoma in magnetic resonance imaging (MRI) images because it is nonradioactive and has no biological damage to brain tissue and more obvious performance in soft tissue components such as tumors, blood vessels, and muscles in MRI images. However, this method is labor‐intensive and time‐consuming work, and cannot guarantee the accuracy of the diagnostic results. Existing osteosarcoma MRI image segmentation methods either aim to model the global context to improve the inner consistency of objects, or multiscale feature fusion to refine the detail of objects along their boundaries, which all ignore the interaction between the body of the object and the object boundary. Therefore, this paper proposes a novel segmentation method for osteosarcoma MRI images based on DecoupleSegNet, which explores the relationship between body feature and edge feature. It can assist doctors in diagnosing osteosarcoma and improve their work efficiency. First, we warp the feature of MRI images through learning a flow field so we can make the object more consistent. We then make further work to optimize the resulting body feature and residual edge feature through explicitly sampling pixels from different parts under decoupled supervision. Through these steps, we finally obtain the final feature map with fine boundaries from the MRI image of osteosarcoma. We take a test by using more than 80,000 osteosarcoma MRI images obtained from three hospitals in China. We find that compared with existing osteosarcoma MRI image segmentation methods, our proposed method achieves 90.51 Intersection of Union % with few parameters on the test, outperforming other models. In the test, we prove that our proposed method has better accuracy and lower resource consumption.

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“…Convolutional neural networks (CNN) are widely applied to computer vision currently. In the aspect of medical image processing about health, CNN performs outstanding [ 50 ]. CNN uses multi-layer superposition to extract from low-level features to high-level features.…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

A Survey on Deep Learning in COVID-19 Diagnosis

Han

Wang

et al. 2022

J. Imaging

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According to the World Health Organization statistics, as of 25 October 2022, there have been 625,248,843 confirmed cases of COVID-19, including 65,622,281 deaths worldwide. The spread and severity of COVID-19 are alarming. The economy and life of countries worldwide have been greatly affected. The rapid and accurate diagnosis of COVID-19 directly affects the spread of the virus and the degree of harm. Currently, the classification of chest X-ray or CT images based on artificial intelligence is an important method for COVID-19 diagnosis. It can assist doctors in making judgments and reduce the misdiagnosis rate. The convolutional neural network (CNN) is very popular in computer vision applications, such as applied to biological image segmentation, traffic sign recognition, face recognition, and other fields. It is one of the most widely used machine learning methods. This paper mainly introduces the latest deep learning methods and techniques for diagnosing COVID-19 using chest X-ray or CT images based on the convolutional neural network. It reviews the technology of CNN at various stages, such as rectified linear units, batch normalization, data augmentation, dropout, and so on. Several well-performing network architectures are explained in detail, such as AlexNet, ResNet, DenseNet, VGG, GoogleNet, etc. We analyzed and discussed the existing CNN automatic COVID-19 diagnosis systems from sensitivity, accuracy, precision, specificity, and F1 score. The systems use chest X-ray or CT images as datasets. Overall, CNN has essential value in COVID-19 diagnosis. All of them have good performance in the existing experiments. If expanding the datasets, adding GPU acceleration and data preprocessing techniques, and expanding the types of medical images, the performance of CNN will be further improved. This paper wishes to make contributions to future research.

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Effectiveness of Learning Systems from Common Image File Types to Detect Osteosarcoma Based on Convolutional Neural Networks (CNNs) Models

Cited by 10 publications

References 31 publications

Optimal Deep Stacked Sparse Autoencoder Based Osteosarcoma Detection and Classification Model

Optimal Deep Stacked Sparse Autoencoder Based Osteosarcoma Detection and Classification Model

A medical assistant segmentation method for MRI images of osteosarcoma based on DecoupleSegNet

A Survey on Deep Learning in COVID-19 Diagnosis

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