Fakieh Bahjat, Al-Ghamdi Abdullah S Al-Malaise, Ragab Mahmoud
Information Systems Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia.
Information Systems Department, HECI School, Dar Alhekma University, Jeddah 22246, Saudi Arabia.
Healthcare (Basel). 2022 Jun 2;10(6):1040. doi: 10.3390/healthcare10061040.
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.
骨肉瘤是一种骨癌，通常在腿部和手臂的长骨中开始发展。由于癌症发病率的增加和针对患者的治疗方案，癌症的检测和分类成为一个困难的过程。骨肉瘤的人工识别需要专业知识且耗时。早期识别骨肉瘤可以降低死亡率。随着新技术的发展，自动检测模型可用于医学图像分类，从而减少对专家的依赖并实现及时识别。近年来，文献中出现了一些计算机辅助检测（CAD）系统，用于使用医学图像对骨肉瘤进行分割和检测。有鉴于此，本研究工作开发了一种基于深度迁移学习的风驱动优化骨肉瘤检测与分类（WDODTL-ODC）方法。所提出的WDODTL-ODC模型旨在确定生物医学图像中骨肉瘤的存在。为实现这一目标，骨肉瘤模型包括基于高斯滤波（GF）的预处理和对比度增强技术。此外，使用SqueezNet模型的深度迁移学习被用作特征提取器。最后，采用带有深度堆叠稀疏自动编码器（DSSAE）的风驱动优化（WDO）算法进行分类过程。仿真结果表明，WDODTL-ODC技术在生物医学图像上骨肉瘤的检测方面优于现有模型。
