An Innovative Solution Based on TSCA-ViT for Osteosarcoma Diagnosis in Resource-Limited Settings

He, Zengxiao; Liu, Jun; Gou, Fangfang; Wu, Jia

doi:10.3390/biomedicines11102740

Cited by 10 publications

(1 citation statement)

References 60 publications

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“…Meanwhile, numerous cancers do not consume the usual image features, it is complex to define the mass nature by trusting only image analyses, and it is very difficult to define whether a patient is suffering from bone cancer or not. Osteosarcoma has numerous subtypes with dissimilar features of pathological [4]. Therefore, all specialists in osteosarcoma analysis consider the pathological analysis.…”

Section: Introductionmentioning

confidence: 99%

Group Teaching Optimization With Deep Learning-Driven Osteosarcoma Detection Using Histopathological Images

Alsubai,

Dutta,

Alghayadh

et al. 2024

IEEE Access

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Osteosarcoma is the most normal kind of cancer that arises in bones, which appears on the surface to resemble earlier types of bone cells that assist in forging new bone tissues, but the tissue in osteosarcoma is weaker and softer than normal bone tissue. The usage of automated techniques for the detection of osteosarcoma has the potential to mitigate the obligations and burdens confronted by pathologists owing to its abundant quantity of cases. Artificial intelligence (AI) has an emerging progress in diagnostic pathology. In recent years, numerous studies using deep learning (DL) techniques to histopathological images (HI) have been published. While several studies claim higher accuracy, they might lack generalization and fall into the pitfall of overfitting owing to the wide range of HI. The study objective is to enhance the diagnosis and detection of osteosarcoma by employing computer-assisted detection (CAD) and diagnoses (CADx). Technique like convolutional neural networks (CNN) make better prognoses for patient conditions and considerably reduce the surgeon's workload. CNN needs to be trained on the massive quantity of data to accomplish a remarkable performance. Therefore, the study presents a novel Group Teaching Optimization Algorithm with Deep Learning-Driven Osteosarcoma Detection on Histopathological Images (GTOADL-ODHI) technique. The purpose of the GTOADL-ODHI technique is to examine the HIs for the detection and classification of osteosarcoma. To accomplish this, the GTOADL-ODHI algorithm applies the Gaussian filtering (GF) method for image pre-processed to become rid of the noise. Besides, the capsule network (CapsNet) model is utilized for the extractor of the feature vector. Furthermore, the hyperparameter selection of the CapsNet approach takes place using the GTOA. Finally, the self-attention bidirectional long short-term memory (SA-BiLSTM) model can be employed for osteosarcoma recognition and classification. The widespread experimental analysis of the GTOADL-ODHI method is tested on the benchmark datasets. The simulation validation reported the optimum solution of the GTOADL-ODHI algorithm related to existing systems concerning distinct aspects.

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Section: Introductionmentioning

confidence: 99%

Group Teaching Optimization With Deep Learning-Driven Osteosarcoma Detection Using Histopathological Images

Alsubai,

Dutta,

Alghayadh

et al. 2024

IEEE Access

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Optimization of edge server group collaboration architecture strategy in IoT smart cities application

Gou,

2024

Peer-to-Peer Netw. Appl.

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Artificial intelligence multiprocessing scheme for pathology images based on transformer for nuclei segmentation

Gou,

Tang,

Liu

et al. 2024

Complex Intell. Syst.

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Malignant tumors are a common cytopathologic disease. Pathological tissue examination is a key tool for diagnosing malignant tumors. Doctors need to manually analyze the images of pathological tissue sections, which is not only time-consuming but also highly subjective, easily leading to misdiagnosis. Most of the existing computer-aided diagnostic techniques focus too much on accuracy when processing pathological tissue images, and do not take into account the problems of insufficient resources in developing countries to meet the training of large models and the difficulty of obtaining medical annotation data. Based on this, this study proposes an artificial intelligence multiprocessing scheme (MSPInet) for digital pathology images of malignant tumors. We use techniques such as data expansion and noise reduction to enhance the dataset. Then we design a coarse segmentation method for cell nuclei of pathology images based on Transformer for Semantic Segmentation and further optimize the segmentation of tumor edges using conditional random fields. Finally, we improve the training strategy for knowledge distillation. As a medical assistive system, the method can quantify and convert complex pathology images into analyzable image information. Experimental results show that our method performs well in terms of segmentation accuracy and also has advantages in terms of time and space efficiency. This makes our technology available to developing countries that are not as well resourced, and equipped in terms of medical care. The teacher model and lightweight student model included in our method achieve 71.6% and 66.1% Intersection over Union (IoU) in cell segmentation respectively, outperforming Swin-unet and CSWin Transformer.

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An Innovative Solution Based on TSCA-ViT for Osteosarcoma Diagnosis in Resource-Limited Settings

Cited by 10 publications

References 60 publications

Group Teaching Optimization With Deep Learning-Driven Osteosarcoma Detection Using Histopathological Images

Group Teaching Optimization With Deep Learning-Driven Osteosarcoma Detection Using Histopathological Images

Optimization of edge server group collaboration architecture strategy in IoT smart cities application

Artificial intelligence multiprocessing scheme for pathology images based on transformer for nuclei segmentation

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