RESEARCH ON DEEP NEURAL NETWORK LEARNING BASED ON IMPROVED BP ALGORITHM by HUANG Pei

“…Deep learning can make the computing model that contains a number of processing layers to learn the data that contains many levels of abstract representation.This kind of learning way in the most advanced speech recognition,visual object recognition,object detection and many other areas,such as biology,genetics and medicine brought significant improvement.Deep learning can find the complex structure of large data,and the convolution neural network as one of the important models of the depth study in the processing of voice,image,video and text,and other aspects of a new breakthrough.It is the use of BP algorithm to guide the machine how to get the error before the layer to adjust the parameters of this layer,so that these parameters are more conducive to the calculation of the model.In view of the shortcomings of traditional BP algorithm,a fast BP algorithm is proposed,which has the disadvantages of slow convergence speed and often falls into local minimum points.The improved convolutional neural network is used to validate the data set MNIST,English character recognition and medical image.The simulation results show the effectiveness of the proposed algorithm.…”
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Adaptive deep residual network for image denoising across multiple noise levels in medical, nature, and satellite images by Mary Charles Sheeba, Christopher Seldev Christopher

“…For example, the model recorded average metrics of MSE 13.61, PSNR 48.81 dB, and SSIM 0.96 on medical images, highlighting its efficacy. These results confirm AdResNet’s suitability for applications requiring high image quality, such as medical and satellite imaging.…”
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Constructing an Artificial Intelligent Deep Neural Network Battery for Tongue Region Segmentation and Tongue Characteristic Recognition by Tian-Xing Yi, Jian-Xin Chen, Xue-Song Wang, Meng-Jie Kou, Qing-Qiong Deng, Xu Wang

“…Conclusions: This two-stage approach not only streamlines the analysis of tongue images but also sets a new benchmark for accuracy in medical image processing in the field.…”
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Mathematical Representation of Color Spaces and Its Role in Communication Systems by Riyadh M. Al-saleem, Baraa M. Al-Hilali, Izz K. Abboud

“…The development of color systems has an impact on visual communication such as television broadcasting systems, medical image processing, and video signal processing, as well as in the field of computer such as graphic equipment and printing.…”
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Annotation-free deep learning for predicting gene mutations from whole slide images of acute myeloid leukemia by Bo-Han Wei, Xavier Cheng-Hong Tsai, Kuo-Jui Sun, Min-Yen Lo, Sheng-Yu Hung, Wen-Chien Chou, Hwei-Fang Tien, Hsin-An Hou, Chien-Yu Chen

“…Abstract The rapid development of deep learning has revolutionized medical image processing, including analyzing whole slide images (WSIs). …”
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Observational Diagnostics: The Building Block of AI-Powered Visual Aid for Dental Practitioners by Ruchika Raj, Ravikumar Rajappa, Vijayalakshmi Murthy, Mahyar Osanlouy, Daniel Lawrence, Mahen Ganhewa, Nicola Cirillo

“…Artificial intelligence (AI) has gained significant traction in medical image analysis, including dentistry, aiding clinicians in making timely and accurate diagnoses. …”
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Heavy and Lightweight Deep Learning Models for Semantic Segmentation: A Survey by Cristina Carunta, Alina Carunta, Calin-Adrian Popa

“…Semantic segmentation is an important computer vision task due to its numerous real-world applications such as autonomous driving, video surveillance, medical image analysis, robotics, augmented reality, among others, and its popularity increased with the development of deep learning approaches. …”
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Automatic Classification of Normal and Cancer Lung CT Images Using Multiscale AM-FM Features by Eman Magdy, Nourhan Zayed, Mahmoud Fakhr

“…Computer-aided diagnostic (CAD) systems provide fast and reliable diagnosis for medical images. In this paper, CAD system is proposed to analyze and automatically segment the lungs and classify each lung into normal or cancer. …”
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The role of radiomics in dentistry and oral radiology by Tannishtha ., Shruthi Hegde, G. Subhas Babu, Vidya Ajila, B.S. Shama

“…This new technology can quantify textural information through mathematical analysis from the region of interest in medical images, which the human eye cannot perceive. In oral and maxillofacial imaging, the use of cone beam computed tomography (CBCT) has been increasing that in turn encourages AI and radiomics research to assist clinicians in early diagnosis and effective treatment planning. …”
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Malaria Diagnosis Using a Lightweight Deep Convolutional Neural Network by Varun Magotra, Mukesh Kumar Rohil

“…The application of convolutional neural network (CNN) and mask-region-based CNN (Mask-RCCN) to the medical domain has really revolutionized medical image analysis. CNNs have been prominently used for identification, classification, and feature extraction tasks, and they have delivered a great performance at these tasks. …”
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An intelligent system for lung CT image denoising using a hybrid WT-NLM filter by S.L. Soniya, T. Ajith Bosco Raj

“…The elimination of noise from the original image is a significant challenge for scientists and this work considers Gaussian, Salt and pepper noises, as medical images are prone to it. Hence, this work presents ways for mitigating noise, while preserving the relevant image information. …”
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Brain CT image classification based on mask RCNN and attention mechanism by Shoulin Yin, Hang Li, Lin Teng, Asif Ali Laghari, Ahmad Almadhor, Michal Gregus, Gabriel Avelino Sampedro

“…The application of machine learning, and block-chain techniques into medical image retrieval, classification and auxiliary diagnosis has become one of the research hotspots at present. …”
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A deep learning ICDNET architecture for efficient classification of histopathological cancer cells using Gaussian noise images by Hui Zong, Wenlong An, Xin Chen, Zhanhui Yang, Heng Zhou, Xiangchao Liu, Jianchu Lin, Chuanyue Zong

“…To address this issue, this study introduces a new hybrid network model, termed ICDNET, designed to fuse global and local features without destroying the integrity of the feature data, thus enhancing the accuracy of medical image classification. The ICDNET model consists of two main features: (i) a serial hierarchical structure composed of global and local feature blocks; and (ii) an Internal Communication Hierarchical Fusion Block (ICHF) and an Efficient Dual Self-Attention (EDA) mechanism. …”
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Multiple sclerosis diagnosis with brain MRI retrieval: A deep learning approach by R.M. Haggag, Eman M. Ali, M.E. Khalifa, Mohamed Taha

“…This study proposes a novel Content-Based Medical Image Retrieval (CBMIR) framework using Convolutional Neural Networks (CNN) and Transfer Learning (TL) for MS diagnosis using MRI data. …”
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Classification of CT scan and X-ray dataset based on deep learning and particle swarm optimization. by Honghua Liu, Mingwei Zhao, Chang She, Han Peng, Mailan Liu, Bo Li

“…Therefore, the proposed method has the potential to effectively classify medical images. The proposed model was verified using a public COVID-19 radiology dataset and a public COVID-19 lung CT scan dataset. …”
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Application and Thinking of Deep Learning in Predicting Lateral Cervical Lymph Node Metastasis of Papillary Thyroid Cancer by Shengli SHAO, Jiheng WANG, Shanting LIU

“…Deep learning is a primary method for medical image recognition or feature extraction. In recent years, deep learning-based ultrasound, CT, cytology, conventional clinical parameters, or multimodal models combining these data have been developed and are expected to achieve routine clinical application. …”
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Development of bone-mimicking resin for 3D printing with enhanced mechanical properties using ceramic filler by Wei-Chun Lin, Chia-Cheng Cheng, Jui-Fu Tang, Kuan-Chung Huang, Kevin Chiou, Ying-Ruei Liu, Yu-An Chen, Zhi-Xian Wu, Yu-Hsien Chu, Yu-Chun Huang, Wei-Chih Chen

“…After obtaining a 3D model file through medical image scanning techniques, including computed tomography and magnetic resonance imaging, it is easy to produce a realistic model with an LCD 3D printer. …”
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A pelvis MR transformer-based deep learning model for predicting lung metastases risk in patients with rectal cancer by Yin Li, Yin Li, Yin Li, Shuang Li, Shuang Li, Ruolin Xiao, Ruolin Xiao, Xi Li, Yongju Yi, Yongju Yi, Liangyou Zhang, Liangyou Zhang, You Zhou, You Zhou, Yun Wan, Chenhua Wei, Liming Zhong, Liming Zhong, Wei Yang, Wei Yang, Lin Yao, Lin Yao, Lin Yao

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A Review of Applying Large Language Models in Healthcare by Qiming Liu, Ruirong Yang, Qin Gao, Tengxiao Liang, Xiuyuan Wang, Shiju Li, Bingyin Lei, Kaiye Gao

“…Next, six specific application areas of LLMs in healthcare are reviewed: disease diagnosis and decision support, dissemination of medical knowledge, medical assistance, medical image analysis, biomedicine, and medical education. …”
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Computational study of transcatheter aortic valve replacement based on patient-specific models—rapid surgical planning for self-expanding valves by Zhuangyuan Meng, Haishan Zhang, Yunhan Cai, Yuan Gao, Changbin Liang, Jun Wang, Xin Chen, Liang Guo, ShengZhang Wang, ShengZhang Wang

“…It also aimed to calculate the risks of postoperative paravalvular leak and atrioventricular conduction block, comparing these risks to clinical outcomes to verify the method’s effectiveness and accuracy. Based on medical images, six cases were established, including the aortic wall, native valve and calcification; one with a bicuspid aortic valve and five with tricuspid aortic valves. …”
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