NHS dentistry is in crisis and many in the profession would agree that it needs a change to ensure future survival. This essay suggests a model based on the psychological hierarchy of needs pyramid to help focus and prioritise what could be the future shape of NHS dentistry to better serve the population as a whole.
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Effective BCDNet-based breast cancer classification model using hybrid deep learning with VGG16-based optimal feature extraction.
BMC Med Imaging
January 2025
Department of Information Technology, Manipal Institute of Technology Bengaluru, Manipal Academy of Higher Education, Manipal, Karnataka, 576104, India.
Problem: Breast cancer is a leading cause of death among women, and early detection is crucial for improving survival rates. The manual breast cancer diagnosis utilizes more time and is subjective. Also, the previous CAD models mostly depend on manmade visual details that are complex to generalize across ultrasound images utilizing distinct techniques.
View Article and Find Full Text PDFHistoNeXt: dual-mechanism feature pyramid network for cell nuclear segmentation and classification.
BMC Med Imaging
January 2025
Department of Information, Third Affiliated Hospital of Naval Medical University, No. 225 Changhai Road, Yangpu District, 200438, Shanghai, China.
Purpose: To develop an end-to-end convolutional neural network model for analyzing hematoxylin and eosin(H&E)-stained histological images, enhancing the performance and efficiency of nuclear segmentation and classification within the digital pathology workflow.
Methods: We propose a dual-mechanism feature pyramid fusion technique that integrates nuclear segmentation and classification tasks to construct the HistoNeXt network model. HistoNeXt utilizes an encoder-decoder architecture, where the encoder, based on the advanced ConvNeXt convolutional framework, efficiently and accurately extracts multi-level abstract features from tissue images.
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School of Electronics and Information Technology, Sun Yat-sen University, Guangzhou 510006, China.
AI-based breast cancer detection can improve the sensitivity and specificity of detection, especially for small lesions, which has clinical value in realizing early detection and treatment so as to reduce mortality. The two-stage detection network performs well; however, it adopts an imprecise ROI during classification, which can easily include surrounding tumor tissues. Additionally, fuzzy noise is a significant contributor to false positives.
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Sci Rep
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School of Intelligent Manufacturing Modern Industry, Xinjiang University, Urumqi, 830046, Xinjiang, China.
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School of Computer Science Technology, Changchun University, Changchun 130022, Jilin Province, China.
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