Advanced COVID-19 CT Image Segmentation Using a Hybrid Undecimated Wavelet Transform, Fuzzy Clustering, and Anisotropic Diffusion Approach

Larbi, Messaouda; Naimi, Hossein Miar; Bourennane, Mohammed

doi:10.18280/ts.400320

Cited by 3 publications

(3 citation statements)

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“…Classification of data is one of the most significant tasks in Machine learning algorithms [16]. A machine learning classifier classifies new data based on the trained classifying attribute from training dataset.…”

Section: Related Workmentioning

confidence: 99%

Deep Feature Extraction with Cubic-SVM for Classification of Brain Tumor

Bourennane,

Naimi,

Mohamed

2024

SEES

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Brain tumors (BT) are fatal and debilitating conditions that shorten the typical lifespan of patients. Patients with BTs who receive inadequate treatment and an incorrect diagnosis have a lower chance of survival. Magnetic resonance imaging (MRI) is often employed to assess the tumor. However, because of the massive quantity of data provided by MRI, early BT detection is a complex and time-consuming procedure in biomedical imaging. As a consequence, an automated and efficient strategy is required. The detection of brain tumors or malignancies has been done using a variety of conventional machine learning (ML) approaches. The manually collected properties, however, provide the main problem with these models. The constraints previously stated are addressed by the fusion deep learning model for binary classification of BTs that is presented in this study. The recommended method combines two different CNN (Efficientnetb0, VGG-19) models that automatically extract features and make use of the feature’s classification using a Cubic SVM classifier model. Additionally, the recommended approach displayed outstanding performance in various classification measures, including Accuracy (99.78%), Precision (99.78%), Recall (99.78%), and F1-Score (99.78%), on the same Kaggle (Br35H) dataset. The proposed strategy performs better than current approaches for classifying BTs from MRI images.

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Section: Related Workmentioning

confidence: 99%

Deep Feature Extraction with Cubic-SVM for Classification of Brain Tumor

Bourennane,

Naimi,

Mohamed

2024

SEES

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“…All performance metrics are based on four numbers derived from applying the classifier to the test set for supervised learning with two classes. The titles of such numbers are True Positives (TP), False Positives (FP), True Negatives (TN), and False Negatives (FN) (Unlersen et al, 2022;Garg et al, 2023;Larbi et al, 2023) 𝐓𝐏 : When there is a disease present, a true positive test result indicates it. 𝐓𝐍 : When a test does not detect a disease when there is none, it is said to be True Negative.…”

Section: Classification Performance Metricsmentioning

confidence: 99%

“…However, treatment may comprise radiotherapy, chemotherapy, surgery, or a mix of other treatments might be performed (Le Rhun and Weller 2020). The most recent advances in imaging technology have shown to be extremely useful in the field of medical imaging (Larbi et al, 2023), notably in the identification and diagnosis of serious human disorders such as brain tumors. There are numerous imaging techniques and modules utilized for diagnosis purpose.…”

Section: Introductionmentioning

confidence: 99%

An efficient hybrid model of CNNs and different kernels of SVM for brain tumor classification

Bourennane,

Naimi

2023

J. Eng. Exact Sci.

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Technological advancements have had a profound impact on various aspects of human existence. The realm of medicine is one major area where technology has made important advances. We will talk specifically about the role technology has had in treating brain tumors, a serious and widespread condition. A large number of people pass away from brain tumors every year. Patients with BTs have a worse likelihood of survival when they receive subpar care and a false diagnosis. The most widely employed technique for detecting brain tumors is magnetic resonance imaging (MRI). Moreover, MRI is extensively utilized in medical imaging and image processing to identify variations in various regions of the body. A convolutional neural network (CNN)-based model was developed in this study to classify brain tumor. Using nine pre-trained CNN models (efficientnetb0, mobilenetv2, nasnetlarge, resnet50, resnet10, googlenet, vgg16, vgg19, and shufflenet), deep features were extracted from the acquired images. Then use a Support Vector Machines (SVM) classifier to classify the deep features. The classification accuracy results obtained from the various kernel functions, namely linear, gaussian, cubic, and quadratic—was then compared. The deep features retrieved from the efficientnetb0 model allowed accurate classification of brain tumors. The classification accuracy achieved using the Gaussian kernel function of SVM was recorded at an impressive 99.78%.

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Adaptive wavelet‐VNet for single‐sample test time adaptation in medical image segmentation

Qian,

Lu,

Zhang

2024

Medical Physics

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BackgroundIn medical image segmentation, a domain gap often exists between training and testing datasets due to different scanners or imaging protocols, which leads to performance degradation in deep learning‐based segmentation models. Given the high cost of manual labeling and the need for privacy protection, it is often challenging to annotate the testing (target) domain data for model fine‐tuning or to collect data from different domains to train domain generalization models. Therefore, using only unlabeled target domain data for test‐time adaptation (TTA) presents a more practical but challenging solution.PurposeTo improve the segmentation accuracy of deep learning‐based models on unseen datasets, and especially to enhance the efficiency and stability of TTA for individual samples from heterogeneous domains.MethodsIn this study, we proposed to dynamically adapt a wavelet‐VNet (WaVNet) to unseen target domains with a hybrid objective function, based on each unlabeled test sample during the test time. We embedded multiscale wavelet coefficients into a V‐Net encoder and adaptively adjusted the spatial and spectral features according to the input, and the model parameters were optimized by three loss functions. We integrated a shape‐aware loss to focus on the foreground segmentations, a Refine loss to correct the incomplete and noisy segmentations caused by domain shifts, and an entropy loss to promote the global consistency of the segmentations. We evaluated the proposed method on multidomain liver and prostate segmentation datasets to assess its advantages over other TTA methods. For the source domain model training of the liver dataset, we used 15 3D MR image samples for training and 5 for validation. Correspondingly, for the prostate dataset, we used 22 3D MR image samples for training and 7 for validation. In the target domain, we used a single 3D MR image sample for adaptation and testing. The total number of testing samples is 60 in the liver dataset (for 3 different domains) and 116 in the prostate dataset (for 6 different domains).ResultsThe proposed method showed the highest segmentation accuracy among all methods, achieving a mean (± SD) Dice coefficient (DSC) of 78.10 ± 5.23% and a mean 95th Hausdorff distance (HD95) of 15.52 ± 5.84 mm on the liver dataset; and a mean DSC of 80.02 ± 3.89% and a mean HD95 of 9.18 ± 3.47 mm on the prostate dataset. The DSC is 11.67% (in absolute terms) and 15.27% higher than that of the baseline (no adaptation) method, for the liver and the prostate datasets, respectively.ConclusionsThe proposed adaptive WaVNet enhanced the image segmentation accuracy from unseen domains during the test time via unsupervised learning and multi‐objective optimization. It can benefit clinical applications where data are scarce or with changing data distributions, including online adaptive radiotherapy. The code will be released at: https://github.com/sanny1226/WaVNet.

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Advanced COVID-19 CT Image Segmentation Using a Hybrid Undecimated Wavelet Transform, Fuzzy Clustering, and Anisotropic Diffusion Approach

Cited by 3 publications

References 35 publications

Deep Feature Extraction with Cubic-SVM for Classification of Brain Tumor

Deep Feature Extraction with Cubic-SVM for Classification of Brain Tumor

An efficient hybrid model of CNNs and different kernels of SVM for brain tumor classification

Adaptive wavelet‐VNet for single‐sample test time adaptation in medical image segmentation

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