A secure two-qubit quantum model for segmentation and classification of brain tumor using MRI images based on blockchain

Amin, Javeria; Anjum, Muhammad Almas; Gul, Nadia; Sharif, Muhammad

doi:10.1007/s00521-022-07388-x

Cited by 21 publications

(12 citation statements)

References 58 publications

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“…This approach, which is commonly used in medical imaging applications, is built on top of a SWin Transformer to extract and down-sample feature maps before feeding them into a Transformer [ 49 ]. This model performs segmentation of tumor pixels with 0.92 dice similarity coefficient [ 50 ]. While transformers have been used successfully in computer vision applications, this technique investigated the use of transformers in medical image processing by replacing the convolutional encoding and decoding procedures in U-Net with a Swin Transformer module and establishing Swin-UNet [ 51 ].…”

Section: Segmentationmentioning

confidence: 99%

A Survey on Deep Learning Based Segmentation, Detection and Classification for 3D Point Clouds

Vinodkumar

Karabulut

Avots

et al. 2023

Entropy

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The computer vision, graphics, and machine learning research groups have given a significant amount of focus to 3D object recognition (segmentation, detection, and classification). Deep learning approaches have lately emerged as the preferred method for 3D segmentation problems as a result of their outstanding performance in 2D computer vision. As a result, many innovative approaches have been proposed and validated on multiple benchmark datasets. This study offers an in-depth assessment of the latest developments in deep learning-based 3D object recognition. We discuss the most well-known 3D object recognition models, along with evaluations of their distinctive qualities.

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Section: Segmentationmentioning

confidence: 99%

A Survey on Deep Learning Based Segmentation, Detection and Classification for 3D Point Clouds

Vinodkumar

Karabulut

Avots

et al. 2023

Entropy

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“…There has been plenty of work carried out in the area of KOA imaging to identify and classify knee diseases. In image processing, feature extraction is an effective step for image representation [ 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 ]. For the recognition of diseases, feature extraction is very helpful to machine learning (ML) algorithms.…”

Section: Related Workmentioning

confidence: 99%

Recognition of Knee Osteoarthritis (KOA) Using YOLOv2 and Classification Based on Convolutional Neural Network

Yunus

Amin

Sharif

et al. 2022

Life

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Knee osteoarthritis (KOA) is one of the deadliest forms of arthritis. If not treated at an early stage, it may lead to knee replacement. That is why early diagnosis of KOA is necessary for better treatment. Manually KOA detection is a time-consuming and error-prone task. Computerized methods play a vital role in accurate and speedy detection. Therefore, the classification and localization of the KOA method are proposed in this work using radiographic images. The two-dimensional radiograph images are converted into three-dimensional and LBP features are extracted having the dimension of N × 59 out of which the best features of N × 55 are selected using PCA. The deep features are also extracted using Alex-Net and Dark-net-53 with the dimensions of N × 1024 and N × 4096, respectively, where N represents the number of images. Then, N × 1000 features are selected individually from both models using PCA. Finally, the extracted features are fused serially with the dimension of N × 2055 and passed to the classifiers on a 10-fold cross-validation that provides an accuracy of 90.6% for the classification of KOA grades. The localization model is proposed with the combination of an open exchange neural network (ONNX) and YOLOv2 that is trained on the selected hyper-parameters. The proposed model provides 0.98 mAP for the localization of classified images. The experimental analysis proves that the presented framework provides better results as compared to existing works.

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“…Features are extracted from pre-trained VGG-16 and input to SVM for discrimination between infected/uninfected cells of malaria with 93.1% accuracy ( 22 ). Custom CNN ( 23 – 31 ) and pre-trained efficientnet-b0 model are used for features extraction and they provided accuracy of 97.74 and 98.82%, respectively ( 20 ). DCNN model is used for the classification of blood smear images with a 94.79% classification accuracy ( 32 ).…”

Section: Related Workmentioning

confidence: 99%

An optimized features selection approach based on Manta Ray Foraging Optimization (MRFO) method for parasite malaria classification

Amin

Sharif

Mallah

et al. 2022

Front. Public Health

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Malaria is a serious and lethal disease that has been reported by the World Health Organization (WHO), with an estimated 219 million new cases and 435,000 deaths globally. The most frequent malaria detection method relies mainly on the specialists who examine the samples under a microscope. Therefore, a computerized malaria diagnosis system is required. In this article, malaria cell segmentation and classification methods are proposed. The malaria cells are segmented using a color-based k-mean clustering approach on the selected number of clusters. After segmentation, deep features are extracted using pre-trained models such as efficient-net-b0 and shuffle-net, and the best features are selected using the Manta-Ray Foraging Optimization (MRFO) method. Two experiments are performed for classification using 10-fold cross-validation, the first experiment is based on the best features selected from the pre-trained models individually, while the second experiment is performed based on the selection of best features from the fusion of extracted features using both pre-trained models. The proposed method provided an accuracy of 99.2% for classification using the linear kernel of the SVM classifier. An empirical study demonstrates that the fused features vector results are better as compared to the individual best-selected features vector and the existing latest methods published so far.

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A secure two-qubit quantum model for segmentation and classification of brain tumor using MRI images based on blockchain

Cited by 21 publications

References 58 publications

A Survey on Deep Learning Based Segmentation, Detection and Classification for 3D Point Clouds

A Survey on Deep Learning Based Segmentation, Detection and Classification for 3D Point Clouds

Recognition of Knee Osteoarthritis (KOA) Using YOLOv2 and Classification Based on Convolutional Neural Network

An optimized features selection approach based on Manta Ray Foraging Optimization (MRFO) method for parasite malaria classification

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