International Journal of Imaging Systems and Technology最新文献

Cerebral palsy (CP) is a movement disorder caused by brain damage. Virtual reality (VR) can improve motor function and daily life activities in CP patients. This systematic review examines the use of non-immersive VR in treating CP children. The objective is to evaluate the effectiveness of non-immersive VR in rehabilitating CP children as a standalone intervention or in combination with traditional therapy. The review follows the PRISMA guidelines and includes a comprehensive search of five bibliographic databases. Two reviewers independently assess the search results, evaluate full-text publications, and extract relevant data. The outcomes were described by the International Classification of Functioning, Disability, and Health for Children and Youth (ICF-CY) framework. A total of 20 English-language studies published between January 2013 and January 2023 are included in the review based on predefined inclusion and exclusion criteria. The findings demonstrate that non-immersive VR, when used in conjunction with traditional therapy, yields positive effects on body structure and function (hand function, grip strength, and upper extremity function), activity (motor function, activities of daily life [ADL], balance), and participation (caretakers' assessment, usability, motivation, and user satisfaction) in CP children. Moreover, non-immersive VR alone is found to be more efficient than traditional therapy in improving manual dexterity in CP children. The non-immersive VR can be effective in rehabilitating CP children, and the review concludes by recommending future research with larger sample sizes and randomized trials to investigate further the potential benefits of non-immersive VR in this population.

Pathological myopia (PM) is a worldwide visual health concern that can cause irreversible vision impairment. It affects up to 20 crore population, causing social and economic burdens. Initial screening of PM using computer-aided diagnosis (CAD) can prevent loss of time and finances for intricate treatments later on. Current research works utilizes complex models that are too resource-intensive or lack explanations behind the categorizations. To emphasize the significance of artificial intelligence for the ophthalmic usage and address the limitations of the current studies, we have designed a mobile-compatible application for smartphone users to detect PM. For this purpose, we have developed a lightweight model, using the enhanced MobileNetV3 architecture integrated with spatial attention (SA) and squeeze-excitation (SE) modules to effectively capture lesion location and channel features. To demonstrate its robustness, the model is tested against three heterogeneous datasets namely PALM, RFMID, and ODIR reporting the area under curve (AUC) score of 0.9983, 0.95, and 0.94, respectively. In order to support PM categorization and demonstrate its correlation with the associated lesions, we have segmented different forms of PM lesion atrophy, which gave us intersection over union (IOU) scores of 0.96 and fscore of 0.97 using the same SA+SE inclusive MobileNetV3 as an encoder. This lesion segmentation can aid ophthalmologists in further analysis and treatment. The optimized and explainable model version is calibrated to develop the smartphone application, which can identify fundus image as PM or normal vision. This app is appropriate for ophthalmologists seeking second opinions or by rural general practitioners to refer PM cases to specialists.

Cervical cancer is a common malignancy worldwide with high incidence and mortality rates in underdeveloped countries. The Pap smear test, widely used for early detection of cervical cancer, aims to minimize missed diagnoses, which sometimes results in higher false-positive rates. To enhance manual screening practices, computer-aided diagnosis (CAD) systems based on machine learning (ML) and deep learning (DL) for classifying cervical Pap cells have been extensively researched. In our study, we introduced a DL-based method named VTCNet for the task of cervical cell classification. Our approach combines CNN-SPPF and ViT components, integrating modules like Focus and SeparableC3, to capture more potential information, extract local and global features, and merge them to enhance classification performance. We evaluated our method on the public SIPaKMeD dataset, achieving accuracies, precision, recall, and F1 scores of 97.16%, 97.22%, 97.19%, and 97.18%, respectively. We also conducted additional experiments on the Herlev dataset, where our results outperformed previous methods. The VTCNet method achieved higher classification accuracy than traditional ML or shallow DL models through this integration. Related codes: https://github.com/Camellia-0892/VTCNet/tree/main.

Multimodal neuroimaging, combining data from different sources, has shown promise in the classification of the Alzheimer's disease (AD) stage. Existing multimodal neuroimaging fusion methods exhibit certain limitations, which require advancements to enhance their objective performance, sensitivity, and specificity for AD classification. This study uses the use of a Pareto-optimal cosine color map to enhance classification performance and visual clarity of fused images. A mobile vision transformer (ViT) model, incorporating the swish activation function, is introduced for effective feature extraction and classification. Fused images from the Alzheimer's Disease Neuroimaging Initiative (ADNI), the Whole Brain Atlas (AANLIB), and Open Access Series of Imaging Studies (OASIS) datasets, obtained through optimized transposed convolution, are utilized for model training, while evaluation is achieved using images that have not been fused from the same databases. The proposed model demonstrates high accuracy in AD classification across different datasets, achieving 98.76% accuracy for Early Mild Cognitive Impairment (EMCI) versus LMCI, 98.65% for Late Mild Cognitive Impairment (LMCI) versus AD, 98.60% for EMCI versus AD, and 99.25% for AD versus Cognitive Normal (CN) in the ADNI dataset. Similarly, on OASIS and AANLIB, the precision of the AD versus CN classification is 99.50% and 96.00%, respectively. Evaluation metrics showcase the model's precision, recall, and F1 score for various binary classifications, emphasizing its robust performance.

An unusual condition of the eye called diabetic retinopathy affects the human retina and is brought on by the blood's constant rise in insulin levels. Loss of vision is the result. Diabetic retinopathy can be improved by receiving an early diagnosis to prevent further damage. A cost-effective method of accumulating medical treatments is through appropriate DR screening. In this work, deep learning framework is introduced for the accurate classification of retinal diseases. The proposed method processes retinal fundus images obtained from databases, addressing noise and artifacts through an improved median filter (ImMF). It leverages the UNet++ model for precise segmentation of the disease-affected regions. UNet++ enhances feature extraction through cross-stage connections, improving segmentation results. The segmented images are then fed as input to the improved gannet optimization-based capsule DenseNet (IG-CDNet) for retinal disease classification. The hybrid capsule DenseNet (CDNet) classifies disease and is optimized using the improved gannet optimization algorithm to boost classification accuracy. Finally, the accuracy and dice score values achieved are 0.9917 and 0.9652 on the APTOS-2019 dataset.

The global impact of COVID-19 has resulted in millions of individuals being afflicted, with a staggering mortality toll of over 16 000 over a span of 2 years. The dearth of resources and diagnostic techniques has had an impact on both emerging and wealthy nations. In response to this, researchers from the domains of engineering and medicine are using deep learning methods to create automated algorithms for detecting COVID-19. This work included the development and comparison of a collaborative deep-learning model for the identification of COVID-19 using CT scan images, in comparison to previous deep learning-based methods. The model underwent an ablation study using publicly accessible COVID-19 CT imaging datasets, with encouraging outcomes. The suggested model might aid doctors and academics in devising tools to expedite the process of determining the optimal therapeutic approach for health professionals, hence reducing the risk of potential problems.

According to the World Health Organization, lung and colon cancers are known for their high mortality rates which necessitate the diagnosis of these cancers at an early stage. However, the limited availability of data such as histopathology images used for diagnosis of these cancers, poses a significant challenge while developing computer-aided detection system. This makes it necessary to keep a check on the number of parameters in the artificial intelligence (AI) model used for the detection of these cancers considering the limited availability of the data. In this work, a customised compact and efficient convolution transformer architecture, termed, C3-Transformer has been proposed for the diagnosis of colon and lung cancers using histopathological images. The proposed C3-Transformer relies on convolutional tokenisation and sequence pooling approach to keep a check on the number of parameters and to combine the advantage of convolution neural network with the advantages of transformer model. The novelty of the proposed method lies in efficient classification of colon and lung cancers using the proposed C3-Transformer architecture. The performance of the proposed method has been evaluated on the ‘LC25000’ dataset. Experimental results shows that the proposed method has been able to achieve average classification accuracy, precision and recall value of 99.30%, 0.9941 and 0.9950, in classifying the five different classes of colon and lung cancer with only 0.0316 million parameters. Thus, the present computer-aided detection system developed using proposed C3-Transformer can efficiently detect the colon and lung cancers using histopathology images with high detection accuracy.

The precise delineation of glioma tumors is of paramount importance for surgical and radiotherapy planning. Presently, the primary drawbacks associated with the manual segmentation approach are its laboriousness and inefficiency. In order to tackle these challenges, a deep learning-based automatic segmentation technique was introduced to enhance the efficiency of the segmentation process. We proposed ADT-UNet, an innovative algorithm for segmenting glioma tumors in MR images. ADT-UNet leveraged attention-dense blocks and Transformer as its foundational elements. It extended the U-Net framework by incorporating the dense connection structure and attention mechanism. Additionally, a Transformer structure was introduced at the end of the encoder. Furthermore, a novel attention-guided multi-scale feature fusion module was integrated into the decoder. To enhance network stability during training, a loss function was devised that combines Dice loss and binary cross-entropy loss, effectively guiding the network optimization process. On the test set, the DSC was 0.933, the IOU was 0.878, the PPV was 0.942, and the Sen was 0.938. Ablation experiments conclusively demonstrated that the inclusion of all the three proposed modules led to enhanced segmentation accuracy within the model. The most favorable outcomes were observed when all the three modules were employed simultaneously. The proposed methodology exhibited substantial competitiveness across various evaluation indices, with the three additional modules synergistically complementing each other to collectively enhance the segmentation accuracy of the model. Consequently, it is anticipated that this method will serve as a robust tool for assisting clinicians in auxiliary diagnosis and contribute to the advancement of medical intelligence technology.