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Готові списки джерел за темами / Medical Image Transfer / Статті в журналах

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Статті в журналах з теми "Medical Image Transfer"

Автор: Grafiati

Опубліковано: 3 червня 2025

Оновлено: 31 липня 2025

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1

Azmi, Azmi, Falath M. M.Mohammed, Saif Al-din M. N, and Azmi Shawkat Abdulbaqi. "Integrating a Secure and Low-Cost WSN Layer with Medical Cloud Computing for Medical Image Transmission." Fusion: Practice and Applications 18, no. 1 (2025): 35–48. https://doi.org/10.54216/fpa.180103.

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Анотація:

Throughout a Wireless Sensor Network (WSN), information collected from the environment is continuously transmitted from one node to the next, and then the main collector or server receives and processes it. With the growth of a network, data transfers within the network also grow dramatically. Medical images increase traffic on a network if they are transmitted. An interlayer transmission protocol (WSN) was developed for this study. Pixels are used to create the medical image using the protocol. A gray-level medical image with 512x512 pixels provided by Brain was used to conduct the study. Med

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2

John, Siju, and S. N. Kumar. "Medical Image Encryption using Latin Image Cipher Algorithm." Journal of Physics: Conference Series 2327, no. 1 (2022): 012070. http://dx.doi.org/10.1088/1742-6596/2327/1/012070.

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Abstract Image processing has significant applications in the health care sector, medical data processing, analysis, storage, and transfer. The Latin Image Cipher algorithm was proposed in this work for the encryption of medical images. The encryption algorithm proposed in this research work comprises Latin square whitening, substitution, and permutation. The efficiency of the algorithm was also validated by inducing noise in the input images. The performance validation of the proposed algorithm was validated by the histogram analysis and correlation analysis. The information entropy measure a

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3

Osmani, Nooshin, Sorayya Rezayi, Erfan Esmaeeli, and Afsaneh Karimi. "Transfer Learning from Non-Medical Images to Medical Images Using Deep Learning Algorithms." Frontiers in Health Informatics 13 (January 6, 2024): 177. http://dx.doi.org/10.30699/fhi.v13i0.549.

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Introduction: Machine learning, especially deep convolutional neural networks (DCNNs), is a popular method for computerizing medical image analysis. This study aimed to develop DCNN models for histopathology image classification utilizing transfer learning.Material and Methods: We utilized 16 different pre-trained DCNNs to analyze the histopathology images from the animal diagnostic laboratory (ADL) database. During the image preprocessing stage, we applied two methods. The first method involved subtracting the mean of ImageNet images from all images. The second method involved subtracting the

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4

Handels, H., E. Rinast, Ch Busch, et al. "Image transfer and computer-supported cooperative diagnosis." Journal of Telemedicine and Telecare 3, no. 2 (1997): 103–7. http://dx.doi.org/10.1258/1357633971930940.

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The KAMEDIN system was designed as a low-cost communication tool as part of a computer-supported cooperative work project that included synchronized user interaction, telepointing and audioconferencing. During a five-month field trial, it was used for medical image transfer and cooperative diagnosis in 14 clinics and medical departments in Germany. During the field test, 297 teleconsultations were performed via ISDN and 875 MByte of data were transferred. An image compression ratio of 2-3 was obtained, so that the total quantity of data transferred corresponded to 14,000-21,000 magnetic resona

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5

Dheepan, G. M. Karpura, Shaik Mohammed Rafee, Prasanthi Badugu, and Sunil Kumar. "A DEEP LEARNING TECHNIQUE FOR EFFICIENT MULTIMEDIA FOR DATA COMPRESSION." ICTACT Journal on Image and Video Processing 14, no. 3 (2024): 3169–74. http://dx.doi.org/10.21917/ijivp.2024.0451.

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Medical image compression plays a pivotal role in efficient data storage and transmission, crucial for modern healthcare systems. This research proposes a convolutional transfer learning technique scheme tailored for multimedia data compression, specifically targeting medical images. In the background, the growing volume of medical imaging data and the demand for efficient storage and transmission underscore the need for innovative compression methods. Leveraging transfer learning from pre-trained convolutional neural networks (CNNs) designed for image recognition tasks, our methodology optimi

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6

Sheelavathy, Hamsavani R, Disha J, Bhavana C, and Bhoomika Rathod. "Image Steganography Technique based on Canny Edge Detection and Hamming Code for Medical Data." International Journal of Engineering and Advanced Technology 8, no. 5s (2019): 23–25. http://dx.doi.org/10.35940/ijeat.e1005.0585s19.

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Image steganography has major role in enhancing the confidentiality of sensitive information related to business information, research data, and health record data and so on. Here the sensitive data considered is Medical data. When the medical image is transmitted through in secure public network, there are chances for medical images to be tampered. To avoid intruders in viewing the sensitive data i.e. Medical information the need of hiding it becomes the foremost criteria. This project mainly aims at enhancing medical integrity. To achieve medical integrity, it is required to hide the medical

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7

Gu, Yi, and Qiankun Zheng. "A Transfer Deep Generative Adversarial Network Model to Synthetic Brain CT Generation from MR Images." Wireless Communications and Mobile Computing 2021 (April 24, 2021): 1–10. http://dx.doi.org/10.1155/2021/9979606.

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Background. The generation of medical images is to convert the existing medical images into one or more required medical images to reduce the time required for sample diagnosis and the radiation to the human body from multiple medical images taken. Therefore, the research on the generation of medical images has important clinical significance. At present, there are many methods in this field. For example, in the image generation process based on the fuzzy C-means (FCM) clustering method, due to the unique clustering idea of FCM, the images generated by this method are uncertain of the attribut

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8

Meng, Qingxin. "Exploration of hyperparameter efficiency for image style transfer." Applied and Computational Engineering 50, no. 1 (2024): 89–96. http://dx.doi.org/10.54254/2755-2721/50/20241240.

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Image style transfer is a popular computer vision technique that aims to merge the content of one image with the style of another to generate a unique, original image with a different aesthetic feel. Numerous models have been developed for various applications in this field, including portrait painting, art creation, and medical image processing, where additional information or annotations could be added to medical images, making them easier to read and understand. This study focuses on optimizing parameters within the pre-trained Visual Geometry Group (VGG19) network architecture, building on

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9

Zhang, Zhanhao. "The transferability of transfer learning model based on ImageNet for medical image classification tasks." Applied and Computational Engineering 18, no. 1 (2023): 143–51. http://dx.doi.org/10.54254/2755-2721/18/20230980.

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Transfer learning with pretrained weights is commonly based on the ImageNet dataset. However, ImageNet does not contain medical images, leaving the transferability of these pretrained weights for medical image classification an open question. The core purpose of this study is to investigate the impact of transfer learning on the accuracy of medical image classification, utilizing ResNet18, VGG11, AlexNet, and MobileNet, which are four of the most widely used neural network models. Specifically, this study aims to determine whether the incorporation of transfer learning techniques leads to sign

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10

M. Lupague, Ryan Marcus Jeremy, Romie C. Mabborang, Prof Alvin G. Bansil, and Melinda M. Lupague. "Assessing Transfer Learning Models for Medical Image Classification: A Comparative Study on Alzheimer’s MRI, Chest CT-Scan, and Chest X-ray Images." International Journal of Recent Technology and Engineering (IJRTE) 12, no. 3 (2023): 59–71. http://dx.doi.org/10.35940/ijrte.c7897.0912323.

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Deep learning has revolutionized the field of neural network models, offering limitless applications in various do- mains. This study focuses on Transfer Learning (TL), a technique leveraging pre-trained deep learning models trained on large datasets for image classification tasks. Specifically, this research explores the effectiveness of various transfer learning models in three medical image datasets: Alzheimer’s MRI images, Chest CT-Scan images, and Chest X-ray images. The main objective of this study is to assess and compare the performance of various TL models, including MobileNetV2, ResN

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11

Ryan, Marcus Jeremy M. Lupague, C. Mabborang Romie, Alvin G. Bansil Prof., and M. Lupague Melinda. "Assessing Transfer Learning Models for Medical Image Classification: A Comparative Study on Alzheimer's MRI, Chest CT-Scan, and Chest X-ray Images." International Journal of Recent Technology and Engineering (IJRTE) 12, no. 3 (2023): 59–71. https://doi.org/10.35940/ijrte.C7897.0912323.

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Анотація:

Deep learning has revolutionized the field of neural network models, offering limitless applications in various do- mains. This study focuses on Transfer Learning (TL), a technique leveraging pre-trained deep learning models trained on large datasets for image classification tasks. Specifically, this research explores the effectiveness of various transfer learning models in three medical image datasets: Alzheimer’s MRI images, Chest CT-Scan images, and Chest X-ray images. The main objective of this study is to assess and compare the performance of various TL models, including MobileNetV2

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12

Putra, Shiva, H. S. Sheshadri, and V. Lokesha. "A Naïve Visual Cryptographic Algorithm for the Transfer of a Compressed Medical Images." International Journal of Recent Contributions from Engineering, Science & IT (iJES) 3, no. 4 (2015): 26. http://dx.doi.org/10.3991/ijes.v3i4.5190.

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The transmission of a suitably compressed image over a bandwidth, over long distances, gives rise towards a new era in the field of information technology. A gradual increase in this appending scenic application, involving the transfer of the images securely over the Ethernet has become an increasingly important aspect to be addressed during thou phenomenon, especially in the transfer of the digital medical images vividly, encapsulated with abundant information related to these images. The compressed medical images of the DICOM format contain certain amount of confidential data, pertaining to

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13

Wang, Xuwen, Yu Zhang, Zhen Guo, and Jiao Li. "Identifying concepts from medical images via transfer learning and image retrieval." Mathematical Biosciences and Engineering 16, no. 4 (2019): 1978–91. http://dx.doi.org/10.3934/mbe.2019097.

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14

Al-Fayoumi, Mustafa A., Ammar Odeh, Ismail Keshta, and Ashraf Ahmad. "Techniques of medical image encryption taxonomy." Bulletin of Electrical Engineering and Informatics 11, no. 4 (2022): 1990–97. http://dx.doi.org/10.11591/eei.v11i4.3850.

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Анотація:

Medical images are one of the most significant and sensitive data types in computer systems. Sending medical images over the internet necessitates using a robust encryption scheme that is resistant to cryptographic attacks. Confidentiality is the most critical part of the three security objectives for information systems security, namely confidentiality, integrity, and availability. Confidentiality is the most critical aspect for the secure storage and transfer of medical images. In this study, we attempt to classify various encryption methods in order to assist researchers in selecting the op

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15

Mustafa, A. Al-Fayoumi, Odeh Ammar, Keshta Ismail, and Ahmad Ashraf. "Techniques of medical image encryption taxonomy." Bulletin of Electrical Engineering and Informatics 11, no. 4 (2022): 1990~1997. https://doi.org/10.11591/eei.v11i4.3850.

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Анотація:

Medical images are one of the most significant and sensitive data types in computer systems. Sending medical images over the internet necessitates using a robust encryption scheme that is resistant to cryptographic attacks. Confidentiality is the most critical part of the three security objectives for information systems security, namely confidentiality, integrity, and availability. Confidentiality is the most critical aspect for the secure storage and transfer of medical images. In this study, we attempt to classify various encryption methods in order to assist researchers in selecting the op

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16

Gu, Chanhoe, and Minhyeok Lee. "Deep Transfer Learning Using Real-World Image Features for Medical Image Classification, with a Case Study on Pneumonia X-ray Images." Bioengineering 11, no. 4 (2024): 406. http://dx.doi.org/10.3390/bioengineering11040406.

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Deep learning has profoundly influenced various domains, particularly medical image analysis. Traditional transfer learning approaches in this field rely on models pretrained on domain-specific medical datasets, which limits their generalizability and accessibility. In this study, we propose a novel framework called real-world feature transfer learning, which utilizes backbone models initially trained on large-scale general-purpose datasets such as ImageNet. We evaluate the effectiveness and robustness of this approach compared to models trained from scratch, focusing on the task of classifyin

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17

Vinod, Manvika. "Detection of Brain Tumor." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 07, no. 10 (2023): 1–11. http://dx.doi.org/10.55041/ijsrem26485.

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Brain tumor detection and segmentation are important tasks in medical image analysis. This project is about creating an image classification model to detect whether an MRI image of a brain has a tumor or not. The model is created using Fast ai, which is a high-level deep learning library built on top of Py Torch. The dataset used in this project contains MRI images of brains with and without tumors. The model is trained using transfer learning with ResNet18 and ResNet34 as the base architectures. After training the model, it is exported and used to make predictions on new images using a simple

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18

M., Sharmila Kumari, and Sudarshana. "AN EFFICIENT METHOD FOR SECURED TRANSFER OF MEDICAL IMAGES." GLOBAL JOURNAL OF ENGINEERING SCIENCE AND RESEARCHES 4, no. 10 (2017): 31–42. https://doi.org/10.5281/zenodo.1006761.

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Transmission of multimedia data is in a secured manner in different channels is found to be one of the complex tasks. Due to heavy traffic in the network,chances of data drop are also very high. In such cases, intruder or any third party can tap the information where the security is compromised. Hence to reduce the transmission time, a novel method is introduced here that provides security of the data as well as compression for faster transmission of data. We have seen substitution cipher scheme has gained prominence in the cryptographic system. The proposed technique considers two arrays name

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19

Akbani, Sufiyan Salim, Adeeba Naaz, Nazish Kausar, and Prof Abdul Razzaque. "Brain Tumor Detection Using Deep Learning." International Journal for Research in Applied Science and Engineering Technology 10, no. 4 (2022): 573–77. http://dx.doi.org/10.22214/ijraset.2022.41321.

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Abstract: One of the most leading death causes in the world is brain tumor. Tumor Detection is one of the most difficult tasks in medical image processing. In fact, the manual classification with human-assisted support can be improper prediction and diagnosis shown by medical evidence. The detection task is too difficult to perform because there is a lot of diversity in the images as brain tumors come in different shapes and textures. Recently, deep learning techniques showed promising results towards improving accuracy of detection and classification of brain tumor from magnetic resonance ima

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20

Jayabharathi S and Dr.V.Ilango. "Transfer Learning Models in Medical Image Anomaly Detection." International Journal of Scientific Research in Science and Technology 12, no. 2 (2025): 1186–89. https://doi.org/10.32628/ijsrst251222678.

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Transfer learning is a common method for moving information from one field to another. In medical imaging applications, transfer from ImageNet has emerged as the de-facto method, in spite of variations in the requirements and picture properties among the domains. The elements that define the usefulness of transfer learning to the medical field are unknown, nevertheless. Recently, the long-held belief that features from the source domain are reused has come under scrutiny.

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21

Yu, Chenglin, and Hailong Pei. "Dynamic Weighting Translation Transfer Learning for Imbalanced Medical Image Classification." Entropy 26, no. 5 (2024): 400. http://dx.doi.org/10.3390/e26050400.

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Medical image diagnosis using deep learning has shown significant promise in clinical medicine. However, it often encounters two major difficulties in real-world applications: (1) domain shift, which invalidates the trained model on new datasets, and (2) class imbalance problems leading to model biases towards majority classes. To address these challenges, this paper proposes a transfer learning solution, named Dynamic Weighting Translation Transfer Learning (DTTL), for imbalanced medical image classification. The approach is grounded in information and entropy theory and comprises three modul

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22

Ebbehoj, Andreas, Mette Østergaard Thunbo, Ole Emil Andersen, Michala Vilstrup Glindtvad, and Adam Hulman. "Transfer learning for non-image data in clinical research: A scoping review." PLOS Digital Health 1, no. 2 (2022): e0000014. http://dx.doi.org/10.1371/journal.pdig.0000014.

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Background Transfer learning is a form of machine learning where a pre-trained model trained on a specific task is reused as a starting point and tailored to another task in a different dataset. While transfer learning has garnered considerable attention in medical image analysis, its use for clinical non-image data is not well studied. Therefore, the objective of this scoping review was to explore the use of transfer learning for non-image data in the clinical literature. Methods and findings We systematically searched medical databases (PubMed, EMBASE, CINAHL) for peer-reviewed clinical stud

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23

Omotosho Moses Oluseyi, Omotosho Moses Oluseyi, Akpan Itoro Udofot Akpan Itoro Udofot, and Edim Bassey Edim Edim Bassey Edim. "Enhancing Medical Image Diagnosis Using Convolutional Neural Network and Transfer Learning." International Journal of Advances in Engineering and Management 7, no. 1 (2025): 131–39. https://doi.org/10.35629/5252-0701131139.

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Medical image diagnosis is crucial for early disease detection and effective treatment planning. Traditional diagnostic methods, while effective, often struggle with the complexities and variability inherent in medical images. Recent advancements in deep learning, particularly through Convolutional Neural Networks (CNNs) and transfer learning, have shown promise in overcoming these challenges. This study investigates the application of CNNs combined with transfer learning to improve diagnostic accuracy and efficiency. We employed several state-of-the-art CNN architectures, including VGG16 and

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24

Alzubaidi, Laith, Muthana Al-Amidie, Ahmed Al-Asadi, et al. "Novel Transfer Learning Approach for Medical Imaging with Limited Labeled Data." Cancers 13, no. 7 (2021): 1590. http://dx.doi.org/10.3390/cancers13071590.

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Deep learning requires a large amount of data to perform well. However, the field of medical image analysis suffers from a lack of sufficient data for training deep learning models. Moreover, medical images require manual labeling, usually provided by human annotators coming from various backgrounds. More importantly, the annotation process is time-consuming, expensive, and prone to errors. Transfer learning was introduced to reduce the need for the annotation process by transferring the deep learning models with knowledge from a previous task and then by fine-tuning them on a relatively small

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25

Vishal, Joshi. "Convolutional Neural Network-Based Medical Image Classification." Journal of Global Research in Multidisciplinary Studies (JGRMS) 1, no. 1 (2025): 1–5. https://doi.org/10.5281/zenodo.14640836.

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Using imaging techniques, human body irregularities are captured. To diagnose, prognosis, and schedule treatment for inconsistency, it is necessary to comprehend the collected images. Generally, qualified medical personnel classify medical images. The inadequacy of human experts, as well as their exhaustion and imprecise estimation methods, restrict the effectiveness of image comprehension performed by qualified medical professionals. The tool for processing images effectively is a CNN. In several image interpretation competitions, they have outperformed human experts. Traditional classificati

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26

Li, Ying, and Peihua Song. "Review of transfer learning in medical image classification." Journal of Image and Graphics 27, no. 3 (2022): 672–86. http://dx.doi.org/10.11834/jig.210814.

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27

Dr Latha M, Dr Manjula G, Dr Raghavendra Y M, Keerthi Kumar M, and Rashmi H C. "Enhancing Skin Cancer Classification on the PH2 Dataset Through Transfer Learning Technique." International Research Journal on Advanced Engineering Hub (IRJAEH) 2, no. 03 (2024): 500–507. http://dx.doi.org/10.47392/irjaeh.2024.0072.

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Skin, the largest organ of the human body, serves as a crucial barrier against external threats. Among the myriad skin diseases, melanoma, or skin cancer, stands out as one of the most perilous and lethal conditions. However, its prognosis dramatically improves when detected early. The advent of advanced diagnostic imaging methods has mitigated the risks associated with cancer treatment, facilitating precise diagnoses and enhancing treatment efficacy. The development and evaluation of image processing algorithms for medical image analysis heavily rely on the availability of medical images. In

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28

Kandel, Ibrahem, and Mauro Castelli. "How Deeply to Fine-Tune a Convolutional Neural Network: A Case Study Using a Histopathology Dataset." Applied Sciences 10, no. 10 (2020): 3359. http://dx.doi.org/10.3390/app10103359.

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Accurate classification of medical images is of great importance for correct disease diagnosis. The automation of medical image classification is of great necessity because it can provide a second opinion or even a better classification in case of a shortage of experienced medical staff. Convolutional neural networks (CNN) were introduced to improve the image classification domain by eliminating the need to manually select which features to use to classify images. Training CNN from scratch requires very large annotated datasets that are scarce in the medical field. Transfer learning of CNN wei

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29

Zhang, Siyuan, Yifan Wang, Jiayao Jiang, Jingxian Dong, Weiwei Yi, and Wenguang Hou. "CNN-Based Medical Ultrasound Image Quality Assessment." Complexity 2021 (July 1, 2021): 1–9. http://dx.doi.org/10.1155/2021/9938367.

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The quality of ultrasound image is a key information in medical related application. It is also an important index in evaluating the performance of ultrasonic imaging equipment and image processing algorithms. Yet, there is still no recognized quantitative standard about medical image quality assessment (IQA) due to the fact that IQA is traditionally regarded as a subjective issue, especially in case of the ultrasound medical images. As such, the medical ultrasound IQA on basis of convolutional neural network (CNN) is quantitatively studied in this paper. Firstly, a dataset with 1063 ultrasoun

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Wulff, Daniel, Mohamad Mehdi, Floris Ernst, and Jannis Hagenah. "Cross Data Set Generalization of Ultrasound Image Augmentation using Representation Learning: A Case Study." Current Directions in Biomedical Engineering 7, no. 2 (2021): 755–58. http://dx.doi.org/10.1515/cdbme-2021-2193.

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Abstract Data augmentation is a common method to make deep learning assessible on limited data sets. However, classical image augmentation methods result in highly unrealistic images on ultrasound data. Another approach is to utilize learning-based augmentation methods, e.g. based on variational autoencoders or generative adversarial networks. However, a large amount of data is necessary to train these models, which is typically not available in scenarios where data augmentation is needed. One solution for this problem could be a transfer of augmentation models between different medical imagin

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Korkut, Şerife Gül, Hatice Kocabaş, and Rifat Kurban. "A Comparative Analysis of Convolutional Neural Network Architectures for Binary Image Classification: A Case Study in Skin Cancer Detection." Karadeniz Fen Bilimleri Dergisi 14, no. 4 (2024): 2008–22. https://doi.org/10.31466/kfbd.1515451.

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In this study, a comprehensive comparative analysis of Convolutional Neural Network (CNN) architectures for binary image classification is presented with a particular focus on the benefits of transfer learning. The performance and accuracy of prominent CNN models, including MobileNetV3, VGG19, ResNet50, and EfficientNetB0, in classifying skin cancer from binary images are evaluated. Using a pre-trained approach, the impact of transfer learning on the effectiveness of these architectures and identify their strengths and weaknesses within the context of binary image classification are investigat

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32

Annadurai, Abirami, Vidhushavarshini Sureshkumar, Dhayanithi Jaganathan, and Seshathiri Dhanasekaran. "Enhancing Medical Image Quality Using Fractional Order Denoising Integrated with Transfer Learning." Fractal and Fractional 8, no. 9 (2024): 511. http://dx.doi.org/10.3390/fractalfract8090511.

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Анотація:

In medical imaging, noise can significantly obscure critical details, complicating diagnosis and treatment. Traditional denoising techniques often struggle to maintain a balance between noise reduction and detail preservation. To address this challenge, we propose an “Efficient Transfer-Learning-Based Fractional Order Image Denoising Approach in Medical Image Analysis (ETLFOD)” method. Our approach uniquely integrates transfer learning with fractional order techniques, leveraging pre-trained models such as DenseNet121 to adapt to the specific needs of medical image denoising. This method enhan

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Dumitru, Delia, Laura Dioșan, Anca Andreica, and Zoltán Bálint. "A Transfer Learning Approach on the Optimization of Edge Detectors for Medical Images Using Particle Swarm Optimization." Entropy 23, no. 4 (2021): 414. http://dx.doi.org/10.3390/e23040414.

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Edge detection is a fundamental image analysis task, as it provides insight on the content of an image. There are weaknesses in some of the edge detectors developed until now, such as disconnected edges, the impossibility to detect branching edges, or the need for a ground truth that is not always accessible. Therefore, a specialized detector that is optimized for the image particularities can help improve edge detection performance. In this paper, we apply transfer learning to optimize cellular automata (CA) rules for edge detection using particle swarm optimization (PSO). Cellular automata p

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Kumari, Sweta, and Madhu Lata Nirmal. "Medical Image Enhancement Using Recurrent Neural Networks Based Tv Homomorphic Filter." International Journal for Research in Applied Science and Engineering Technology 10, no. 2 (2022): 1229–39. http://dx.doi.org/10.22214/ijraset.2022.40483.

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Abstract: Image Enhancement is one of the important requirements in Digital Image Processing which is important in making an image useful for various applications which can be seen in the areas of Digital photography, Medicine, Geographic Information System, Industrial Inspection, Law Enforcement and many more Digital Image Applications. Image Enhancement is used to improve the quality of poor images. X-ray image contains a large amount of information and became important basis in the process of medical diagnosis. The X-ray image has large gray dynamic range but low contrast. This work propose

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Ritahani Ismail, Amelia, Syed Qamrun Nisa, Shahida Adila Shaharuddin, Syahmi Irdina Masni, and Syaza Athirah Suharudin Amin. "Utilising VGG-16 of Convolutional Neural Network for Medical Image Classification." International Journal on Perceptive and Cognitive Computing 10, no. 1 (2024): 113–18. http://dx.doi.org/10.31436/ijpcc.v10i1.460.

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Medical image classification, which involves accurately classifying anomalies or abnormalities within images, is an important area of attention in healthcare domain. It requires a fast and exact classification to ensure appropriate and timely treatment to the patients. This paper introduces a model based on Convolutional Neural Network (CNN) that utilises the VGG16 architecture for medical image classification, specifically in brain tumour and Alzheimer dataset. The VGG16 architecture, is known for its remarkable ability to extract important features, that is crucial in medical image classific

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Zhang, Gengyuan. "A ResNet based transfer learning pulmonary medical image disease detection system." Applied and Computational Engineering 88, no. 1 (2024): 195–200. http://dx.doi.org/10.54254/2755-2721/88/20241699.

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Medical imaging represents a significant application of deep learning. In response to the vast domain of medical image analysis and the limitations and inefficiencies of conventional methods for diagnosing brain diseases, this paper explores the application of an enhanced ResNet-50 model in the classification of brain CT images. The goal is to improve the detection and classification accuracy of aneurysms, cancer, and malignant tumors. The study utilized 259 images, undergoing training, validation, and testing processes to verify the model's efficacy. The ResNet-50 model addresses the issue of

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Gomathi, R., and S. Selvakumaran. "A Novel Medical Image Segmentation Model with Domain Generalization Approach." International Journal of Electrical and Electronics Research 10, no. 2 (2022): 312–19. http://dx.doi.org/10.37391/ijeer.100242.

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In deep learning-based computing vision for image processing, image segmentation is a prominent issue. There is promising generalisation performance in the medical image segmentation sector for approaches using domain generalisation (DG). Single domain generalisation (SDG) is a more difficult problem than conventional generalisation (DG), which requires numerous source domains to be accessible during network training, as opposed to conventional generalisation (DG). Color medical images may be incorrectly segmented because of the augmentation of the full image in order to increase model general

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Ayana, Gelan, Jinhyung Park, Jin-Woo Jeong, and Se-woon Choe. "A Novel Multistage Transfer Learning for Ultrasound Breast Cancer Image Classification." Diagnostics 12, no. 1 (2022): 135. http://dx.doi.org/10.3390/diagnostics12010135.

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Breast cancer diagnosis is one of the many areas that has taken advantage of artificial intelligence to achieve better performance, despite the fact that the availability of a large medical image dataset remains a challenge. Transfer learning (TL) is a phenomenon that enables deep learning algorithms to overcome the issue of shortage of training data in constructing an efficient model by transferring knowledge from a given source task to a target task. However, in most cases, ImageNet (natural images) pre-trained models that do not include medical images, are utilized for transfer learning to

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Wang, Wen Bo, Li Juan Zhou, and Li Fei. "An Improved Algorithm Based on Retinex Theory for X-Ray Medical Image." Advanced Materials Research 772 (September 2013): 233–38. http://dx.doi.org/10.4028/www.scientific.net/amr.772.233.

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Retinex theory combined the elements of images and visual.This paper improved the Retinex-based medical image enhancement method, It can get better brightness by using the neural network logarithmic The S-shaped LogSig transfer function instead of the original MSR logarithm function. Based on this, the paper presents a composite LRA (LogSig Retinex Algorithm) algorithm, and analysed the shortcomings of the original Retinex algorithm applied to the X-ray medical image analysis, described the advantage of the composite LRA algorithm is better than traditional Retinex algorithm on the X-ray medic

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Zhang, YiNan, and MingQiang An. "Deep Learning- and Transfer Learning-Based Super Resolution Reconstruction from Single Medical Image." Journal of Healthcare Engineering 2017 (2017): 1–20. http://dx.doi.org/10.1155/2017/5859727.

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Medical images play an important role in medical diagnosis and research. In this paper, a transfer learning- and deep learning-based super resolution reconstruction method is introduced. The proposed method contains one bicubic interpolation template layer and two convolutional layers. The bicubic interpolation template layer is prefixed by mathematics deduction, and two convolutional layers learn from training samples. For saving training medical images, a SIFT feature-based transfer learning method is proposed. Not only can medical images be used to train the proposed method, but also other

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Ajili, Sondes, Mohamed Ali Hajjaji, and Abdellatif Mtibaa. "Crypto-Watermarking Algorithm Using Weber’s Law and AES: A View to Transfer Safe Medical Image." Scientific Programming 2021 (August 13, 2021): 1–22. http://dx.doi.org/10.1155/2021/5559191.

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We propose a novel method for medical image watermarking in the DCT domain using the AES encryption algorithm. First, we decompose the original medical image into subblocks of 8 × 8. Besides, we apply the DCT and the quantization, respectively, to each subblock. However, in the DCT domain, an adequate choice of the DCT coefficients according to the quantization table in the middle frequencies band is performed. After that, we embed the patient’s data into the corresponding medical image. The insertion step is carried out just after the quantization phase. To increase the robustness, we encrypt

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Jiang, Chenhan, Shaoju Wang, Xiaodan Liang, Hang Xu, and Nong Xiao. "ElixirNet: Relation-Aware Network Architecture Adaptation for Medical Lesion Detection." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 07 (2020): 11093–100. http://dx.doi.org/10.1609/aaai.v34i07.6765.

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Most advances in medical lesion detection network are limited to subtle modification on the conventional detection network designed for natural images. However, there exists a vast domain gap between medical images and natural images where the medical image detection often suffers from several domain-specific challenges, such as high lesion/background similarity, dominant tiny lesions, and severe class imbalance. Is a hand-crafted detection network tailored for natural image undoubtedly good enough over a discrepant medical lesion domain? Is there more powerful operations, filters, and sub-net

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Jiang, Xiaoyan, Zuojin Hu, Shuihua Wang, and Yudong Zhang. "Deep Learning for Medical Image-Based Cancer Diagnosis." Cancers 15, no. 14 (2023): 3608. http://dx.doi.org/10.3390/cancers15143608.

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(1) Background: The application of deep learning technology to realize cancer diagnosis based on medical images is one of the research hotspots in the field of artificial intelligence and computer vision. Due to the rapid development of deep learning methods, cancer diagnosis requires very high accuracy and timeliness as well as the inherent particularity and complexity of medical imaging. A comprehensive review of relevant studies is necessary to help readers better understand the current research status and ideas. (2) Methods: Five radiological images, including X-ray, ultrasound (US), compu

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Jae Lim, Myung, Da Eun Kim, Dong Kun Chung, Hoon Lim, and Young Man Kwon. "Deep Convolution Neural Networks for Medical Image Analysis." International Journal of Engineering & Technology 7, no. 3.33 (2018): 115. http://dx.doi.org/10.14419/ijet.v7i3.33.18588.

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Breast cancer is a highly contagious disease that has killed many people all over the world. It can be fully recovered from early detection. To enable the early detection of the breast cancer, it is very important to classify accurately whether it is breast cancer or not. Recently, the deep learning approach method on the medical images such as these histopathologic images of the breast cancer is showing higher level of accuracy and efficiency compared to the conventional methods. In this paper, the breast cancer histopathological image that is difficult to be distinguished was analyzed visual

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Ramasamy, Meena Prakash, Thayammal Subburaj, Valarmathi Krishnasamy, and Vimala Mannarsamy. "Performance analysis of breast cancer histopathology image classification using transfer learning models." International Journal of Electrical and Computer Engineering (IJECE) 14, no. 5 (2024): 6006. http://dx.doi.org/10.11591/ijece.v14i5.pp6006-6015.

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Convolutional neural networks (CNN) which are deep learning-based methods are being currently successfully deployed and have gained much popularity in medical image analysis. CNN can handle enormous amounts of medical data which makes it possible for accurate detection and classification of breast cancer from histopathological images. In the proposed method, we have implemented transfer learning-based classification of breast cancer histopathological images using DenseNet121, DenseNet201, VGG16, VGG19, InceptionV3, and MobileNetV2 and made a performance analysis of the different models on the

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MURAKAWA, Masahiro. "Transfer Learning in Infrastructure/Medical Image diagnosis Towards Society5.0." Proceedings of Mechanical Engineering Congress, Japan 2020 (2020): F01105. http://dx.doi.org/10.1299/jsmemecj.2020.f01105.

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Kora, Padmavathi, Chui Ping Ooi, Oliver Faust, et al. "Transfer learning techniques for medical image analysis: A review." Biocybernetics and Biomedical Engineering 42, no. 1 (2022): 79–107. http://dx.doi.org/10.1016/j.bbe.2021.11.004.

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Zhou, Shiwei, Haifeng Zhao, Leilei Ma, and Dengdi Sun. "Semantic knowledge transfer for semi-supervised medical image segmentation." Engineering Applications of Artificial Intelligence 158 (October 2025): 111235. https://doi.org/10.1016/j.engappai.2025.111235.

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49

Privalov, M., and M. Stupina. "Application of the transfer learning to the medical images texture classification task." E3S Web of Conferences 224 (2020): 01020. http://dx.doi.org/10.1051/e3sconf/202022401020.

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This study is conducted to determine effectiveness and perspectives of application of the transfer learning approach to the medical images classification task. There are a lot of medical studies that involve image acquisition, such as XRay radiography, ultrasonic scanning, computer tomography (CT), magnetic resonance imaging (MRI) etc. Besides those medical procedures there are different operations that use medical images processing including but not limited to digital radiograph reconstruction (DRR), radiotherapy planning, brachy therapy planning. All those tasks could be effectively performe

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Marakumbi, Prakash, and Satish Bhairannawar. "Efficient reconfigurable architecture to enhance medical image security." Indonesian Journal of Electrical Engineering and Computer Science 30, no. 3 (2023): 1516. http://dx.doi.org/10.11591/ijeecs.v30.i3.pp1516-1524.

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Medical images are one of the most critical and sensitive types of data in information systems. For the secure storage and transfer of medical images, confidentiality is the most important aspect. This paper presents efficient embedding technique to enhance medical image security. The Gaussian filters are used as preprocessing to remove high frequency components and then applied to cumulative distribution function (CDF) 5/3 wavelet to obtain LL band features. Similarly, the LL band features of cover image are obtained. The alpha bending technique combines both the LL band features of cover and

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