Fractional‐Harris hawks optimization‐based generative adversarial network for osteosarcoma detection using Renyi entropy‐hybrid fusion

Badashah, Syed Jahangir; Basha, S. Shafiulla; Ahamed, Shaik Rafi; Rao, Sunil

doi:10.1002/int.22539

Cited by 23 publications

(9 citation statements)

References 29 publications

(49 reference statements)

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“…Various osteosarcoma detection methods are used for early detection of osteosarcoma, but evaluation of slides under the microscope to detect the extent of tumor necrosis and tumor outcome remains a major challenge in the medical field. Therefore, Badashah et al [ 29 ] developed an effective detection method for early detection of osteosarcoma using the proposed fractional-Harris Hawks optimization-based generative adversarial network (F-HHO-based GAN). F-HHO was designed by integrating fractional calculus and HHO, while GAN extracted image features based on F-HHO algorithm and cell segmentation process, allowing better performance in terms of accuracy, sensitivity, and specificity.…”

Section: Related Workmentioning

confidence: 99%

Rethinking U-Net from an Attention Perspective with Transformers for Osteosarcoma MRI Image Segmentation

Ouyang

Yang

Gou

et al. 2022

Computational Intelligence and Neuroscience

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Osteosarcoma is one of the most common primary malignancies of bone in the pediatric and adolescent populations. The morphology and size of osteosarcoma MRI images often show great variability and randomness with different patients. In developing countries, with large populations and lack of medical resources, it is difficult to effectively address the difficulties of early diagnosis of osteosarcoma with limited physician manpower alone. In addition, with the proposal of precision medicine, existing MRI image segmentation models for osteosarcoma face the challenges of insufficient segmentation accuracy and high resource consumption. Inspired by transformer’s self-attention mechanism, this paper proposes a lightweight osteosarcoma image segmentation architecture, UATransNet, by adding a multilevel guided self-aware attention module (MGAM) to the encoder-decoder architecture of U-Net. We successively perform dataset classification optimization and remove MRI image irrelevant background. Then, UATransNet is designed with transformer self-attention component (TSAC) and global context aggregation component (GCAC) at the bottom of the encoder-decoder architecture to perform integration of local features and global dependencies and aggregation of contexts to learned features. In addition, we apply dense residual learning to the convolution module and combined with multiscale jump connections, to improve the feature extraction capability. In this paper, we experimentally evaluate more than 80,000 osteosarcoma MRI images and show that our UATransNet yields more accurate segmentation performance. The IOU and DSC values of osteosarcoma are 0.922 ± 0.03 and 0.921 ± 0.04, respectively, and provide intuitive and accurate efficient decision information support for physicians.

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Section: Related Workmentioning

confidence: 99%

Rethinking U-Net from an Attention Perspective with Transformers for Osteosarcoma MRI Image Segmentation

Ouyang

Yang

Gou

et al. 2022

Computational Intelligence and Neuroscience

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“…They pointed out that DSC was considered satisfactory between 0.61 and 0.80, and almost perfect or excellent between 0.81 and 1.00. The study shows that the average DSC of manual segmentation is 0.91, and the average reading time is about 616.8 ± 390.1 s. The F-HHO model proposed by Badashah et al [ 48 ] is a generative adversarial network (GAN) based on the Fractional-Harris Hawks optimizer, which performs the detection of osteosarcoma by extracting characteristics from the images during the cell image segmentation process. F-HHO has reached more than 95% in accuracy, sensitivity, and specificity.…”

Section: Related Workmentioning

confidence: 99%

AI-Assisted Diagnosis and Decision-Making Method in Developing Countries for Osteosarcoma

Tang

Huang

et al. 2022

Healthcare

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Osteosarcoma is a malignant tumor derived from primitive osteogenic mesenchymal cells, which is extremely harmful to the human body and has a high mortality rate. Early diagnosis and treatment of this disease is necessary to improve the survival rate of patients, and MRI is an effective tool for detecting osteosarcoma. However, due to the complex structure and variable location of osteosarcoma, cancer cells are highly heterogeneous and prone to aggregation and overlap, making it easy for doctors to inaccurately predict the area of the lesion. In addition, in developing countries lacking professional medical systems, doctors need to examine mass of osteosarcoma MRI images of patients, which is time-consuming and inefficient, and may result in misjudgment and omission. For the sake of reducing labor cost and improve detection efficiency, this paper proposes an Attention Condenser-based MRI image segmentation system for osteosarcoma (OMSAS), which can help physicians quickly locate the lesion area and achieve accurate segmentation of the osteosarcoma tumor region. Using the idea of AttendSeg, we constructed an Attention Condenser-based residual structure network (ACRNet), which greatly reduces the complexity of the structure and enables smaller hardware requirements while ensuring the accuracy of image segmentation. The model was tested on more than 4000 samples from two hospitals in China. The experimental results demonstrate that our model has higher efficiency, higher accuracy and lighter structure for osteosarcoma MRI image segmentation compared to other existing models.

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“…In [ 13 ], a robust detection technique has been introduced based on Fractional-Harris Hawks Optimization (F-HHO) related generative adversarial network (GAN) to detect osteosarcoma at an earlier phase. Now, the presented method was intended by the incorporation of HHO and Fractional Calculus, correspondingly.…”

Section: Related Workmentioning

confidence: 99%

Design of a Honey Badger Optimization Algorithm with a Deep Transfer Learning-Based Osteosarcoma Classification Model

Vaiyapuri

Jothi

Narayanasamy

et al. 2022

Cancers

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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.

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Fractional‐Harris hawks optimization‐based generative adversarial network for osteosarcoma detection using Renyi entropy‐hybrid fusion

Cited by 23 publications

References 29 publications

Rethinking U-Net from an Attention Perspective with Transformers for Osteosarcoma MRI Image Segmentation

Rethinking U-Net from an Attention Perspective with Transformers for Osteosarcoma MRI Image Segmentation

AI-Assisted Diagnosis and Decision-Making Method in Developing Countries for Osteosarcoma

Design of a Honey Badger Optimization Algorithm with a Deep Transfer Learning-Based Osteosarcoma Classification Model

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