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- research-articleJuly 2024
MDUNet: deep-prior unrolling network with multi-parameter data integration for low-dose computed tomography reconstruction
Machine Vision and Applications (MVAA), Volume 35, Issue 4https://doi.org/10.1007/s00138-024-01568-6AbstractThe goal of this study is to reconstruct a high-quality computed tomography (CT) image from low-dose acquisition using an unrolling deep learning-based reconstruction network with less computational complexity and a more generalized model. We ...
- research-articleJuly 2024
Towards scanning electron microscopy image denoising: a state-of-the-art overview, benchmark, taxonomies, and future direction
Machine Vision and Applications (MVAA), Volume 35, Issue 4https://doi.org/10.1007/s00138-024-01573-9AbstractScanning electron microscope (SEM) enables imaging of micro-nano scale objects. It is an analytical tool widely used in the material, earth and life sciences. However, SEM images often suffer from high noise levels, influenced by factors such as ...
- research-articleJune 2024
Performance analysis of various deep learning models based on Max-Min CNN for lung nodule classification on CT images
Machine Vision and Applications (MVAA), Volume 35, Issue 4https://doi.org/10.1007/s00138-024-01569-5AbstractLung cancer remains one of the leading causes of cancer-related deaths worldwide, underlining the urgent need for accurate and early detection and classification methods. In this paper, we present a comprehensive study that evaluates and compares ...
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- research-articleJuly 2023
A high-level feature channel attention UNet network for cholangiocarcinoma segmentation from microscopy hyperspectral images
Machine Vision and Applications (MVAA), Volume 34, Issue 5https://doi.org/10.1007/s00138-023-01418-xAbstractPathological diagnosis is the gold standard for the diagnosis of cholangiocarcinoma. The manual segmentation of pathology sections is time-consuming. Automatic segmentation has become a clinical requirement. Recently, the UNet network has been ...
- research-articleDecember 2022
RAU-Net: U-Net network based on residual multi-scale fusion and attention skip layer for overall spine segmentation
Machine Vision and Applications (MVAA), Volume 34, Issue 1https://doi.org/10.1007/s00138-022-01360-4AbstractSpine segmentation is necessary for the clinical quantitative analysis of computed tomography (CT) images and plays an important role in the early diagnosis and treatment of spine diseases. However, because of the different fields of view of ...
- research-articleNovember 2022
MURA-objects: a radioactive bone imaging lesion detection dataset
Machine Vision and Applications (MVAA), Volume 33, Issue 6https://doi.org/10.1007/s00138-022-01347-1AbstractThe computer-aided diagnosis technology needs to determine whether the bone image is abnormal and needs to locate the location of the lesions accurately. However, there are few publicly available detection datasets of bone lesions. Therefore, for ...
- research-articleJuly 2022
A novel method for 3D knee anatomical landmark localization by combining global and local features
Machine Vision and Applications (MVAA), Volume 33, Issue 4https://doi.org/10.1007/s00138-022-01303-zAbstractLandmark localization with neural networks had gained popularity in recent years. However, due to the high dimensionality and large size of medical images, current neural network models still have problems such as information loss with deeper ...
- research-articleMarch 2022
Potential escalator-related injury identification and prevention based on multi-module integrated system for public health
Machine Vision and Applications (MVAA), Volume 33, Issue 2https://doi.org/10.1007/s00138-022-01273-2AbstractEscalator-related injuries threaten public health with the widespread use of escalators. The existing studies tend to focus on after-the-fact statistics, reflecting on the original design and use of defects to reduce the impact of escalator-...
- research-articleMarch 2022
Identification of facial skin diseases from face phenotypes using FSDNet in uncontrolled environment
Machine Vision and Applications (MVAA), Volume 33, Issue 2https://doi.org/10.1007/s00138-021-01259-6AbstractFacial skin diseases occur due to multiple reasons. They may have different or similar phenotypic signs and may psychologically and physically impact the affected person. Therefore, early detection, diagnosis, and prognosis of such skin diseases ...
- research-articleJanuary 2022
Automated diagnosis of diverse coffee leaf images through a stage-wise aggregated triple deep convolutional neural network
Machine Vision and Applications (MVAA), Volume 33, Issue 1https://doi.org/10.1007/s00138-022-01277-yAbstractDue to the struggles of developing countries in coping with widespread coffee leaf diseases and infestations, the quality and quantity of coffee-based commodities have reduced significantly. This paper proposes a solution to this problem using ...
- research-articleJanuary 2022
Wavelet and PCA-based glaucoma classification through novel methodological enhanced retinal images
Machine Vision and Applications (MVAA), Volume 33, Issue 1https://doi.org/10.1007/s00138-021-01263-wAbstractIn this paper, we have proposed a systematic retinal image enhancement and classification method. The proposed method deals with balancing all the visual and technical aspects of the image for glaucoma diagnosis. Initially, similar 3D image blocks ...
- research-articleJanuary 2022
An empirical study of different machine learning techniques for brain tumor classification and subsequent segmentation using hybrid texture feature
Machine Vision and Applications (MVAA), Volume 33, Issue 1https://doi.org/10.1007/s00138-021-01262-xAbstractBrain tumor classification and segmentation for different weighted MRIs are among the most tedious tasks for many researchers due to the high variability of tumor tissues based on texture, structure, and position. Our study is divided into two ...
- research-articleNovember 2021
Integration of 2D iteration and a 3D CNN-based model for multi-type artifact suppression in C-arm cone-beam CT
Machine Vision and Applications (MVAA), Volume 32, Issue 6https://doi.org/10.1007/s00138-021-01240-3AbstractLimiting the potential risks associated with radiation exposure is critically important when obtaining a diagnostic image. However, lowering the level of radiation may cause excessive noise and artifacts in computed tomography (CT) scans. In this ...
- research-articleJuly 2021
Computer-aided automatic detection of acrylamide in deep-fried carbohydrate-rich food items using deep learning
Machine Vision and Applications (MVAA), Volume 32, Issue 4https://doi.org/10.1007/s00138-021-01204-7AbstractDeep-fried carbohydrate-rich foods items such as potato chips and French fries are one of the most popular snack foods consumed across the globe. In the production of these carbohydrate-rich foods items, a compound known as acrylamide is formed ...
- research-articleMay 2021
A stacked dense denoising–segmentation network for undersampled tomograms and knowledge transfer using synthetic tomograms
Machine Vision and Applications (MVAA), Volume 32, Issue 3https://doi.org/10.1007/s00138-021-01196-4AbstractOver recent years, many approaches have been proposed for the denoising or semantic segmentation of X-ray computed tomography (CT) scans. In most cases, high-quality CT reconstructions are used; however, such reconstructions are not always ...
- research-articleJanuary 2021
A weighted feature transfer gan for medical image synthesis
Machine Vision and Applications (MVAA), Volume 32, Issue 1https://doi.org/10.1007/s00138-020-01152-8AbstractRecent studies have shown that CycleGAN is a highly influential medical image synthesis model. However, the lack of sufficient constraints and the bottleneck layer in auto-encoder network usually lead to blurry image and meaningless features, ...
- research-articleJanuary 2021
A cognitive vision method for the detection of plant disease images
Machine Vision and Applications (MVAA), Volume 32, Issue 1https://doi.org/10.1007/s00138-020-01150-wAbstractFood security, which has currently attracted much attention, requires minimizing crop damage by timely detection of plant diseases. Therefore, the automatic identification and diagnosis of plant diseases are highly desired in agricultural ...
- research-articleJanuary 2021
Optimal feature level fusion for secured human authentication in multimodal biometric system
Machine Vision and Applications (MVAA), Volume 32, Issue 1https://doi.org/10.1007/s00138-020-01146-6AbstractThe rising demand for high security and reliable authentication schemes, led to the development of the unimodal biometric system so that the multimodal biometric system has emerged. The multimodal biometric system will use more than one biometric ...