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Biomedical Technology 2024年 8卷 1-16页

作者： Mohapatra, Subhashree Jeji, Pukhraj Singh Pati, Girish Kumar Mishra, Manohar Swarnkar, Tripti Department of Computer Science & Engineering SOA Deemed to be University Odisha India Department of Gastroenterology Apollo Hospital Bhubaneswar India Department of Gastroenterology & Hepatobiliary Sciences IMS & SUM Hospital SOA Deemed to be University Odisha India Department of Electrical and Electronics SOA Deemed to be University Odisha India Department of Computer Application National Institute of Technology (NIT) Raipur India

Until now various deep convolutional neural networks are designed and trained for the purpose of classifying different medical conditions related to the domain of gastroenterology. Most of the study carried out have considered publicly available datasets to train the classification networks. Nevertheless, the main motive for carrying out different works in the field gastroenterology is to administer the developed models in healthcare centers in real-world set-ups. For doing so, it is important to check the generalizing ability of the designed systems by regulating them so as to classify endoscopy images captured in a specific hospital. In this regard, the foremost work completed is the collection of the endoscopy data from the hospital and then correctly annotating the images taking the help of a senior endoscopist with experience of more than 5 years. Once the data annotation is completed, the images are segregated into the class of normal and abnormal endoscopy images. Four different models are designed in the current work based on deep learning models, transfer learning models and ensemble approaches, and trained to classify the hospital endoscopy data as normal or abnormal. The models are then tested and evaluated based on various performance measures. It is observed from the comparative analysis that the transfer learning-based ensemble model has the best generalizing ability and gives the best specificity of 100 %. It is believed that deep learning-based models can assist endoscopists in add-on to human prediction efficiency. © 2024 The Authors

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ABID: Attention-Based Bengali Image Description 3rd

ABID: Attention-Based Bengali Image Description

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3rd International Conference on Communication, Devices and Computing, ICCDC 2021

作者： Das, Bidyut Sekh, Arif Ahmed Majumder, Mukta Phadikar, Santanu Department of Information Technology Haldia Institute of Technology Haldia India School of Computer Science and Engineering XIM University Bhubaneswar India Department of Computer Science and Application University of North Bengal Darjeeling India Department of Computer Science and Engineering Maulana Abul Kalam Azad University of Technology West Bengal India

ISBN: (纸本)9789811691539

Image caption or description generation is a fundamental problem of artificial intelligence. It requires both knowledge, natural language processing, and computer vision together. It automatically produces description(s) from an image. The Bengali language is the fifth language spoken widely in the world. It is considered in many areas of research and development. Various established datasets are available in the literature for image caption generation in English since no such standard dataset exists for Bengali. This paper proposes a model for automatic image captioning in the Bengali language. This study uses a Bengali dataset to train the encoder–decoder neural network model. The proposed model trained with two descriptions per image and achieved a 0.67 BLEU-1 score. We expect that a far better result is obtained with a larger and more diverse dataset. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Neural network models

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FViT: Finetuned Vision Transformer for Improved Brain Tumor Classification

FViT: Finetuned Vision Transformer for Improved Brain Tumor ...

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Ambient Intelligence in Health Care (ICAIHC), International Conference on

作者： Lalini Chandra Tej Bahadur Chandra Anirud Rathy Anushka Rai Department of Computer Application National Institute of Technology Raipur Chhattisgarh India School of Computer Science Engineering and Technology Bennett University Greater Noida UP India

ISBN: (数字)9798331530259

ISBN: (纸本)9798331530266

Timely detection and treatment of brain tumors (BT) remain a critical challenge due to their rapid growth and complex morphologies. Recent deep learning (DL) approaches and large vision models (LVMs) have promising potential in early and accurate diagnosis of BTs. However, the optimal tuning of hyperparameters is crucial for increasing the model’s efficacy and robustness. Large vision models have significant potential for improving the accuracy of diagnosing BT using MRI images. Additionally, we evaluated and compared the performance of recent DL models ResNet50, VGG16, and Long Short-Term Memory (LSTM) using BT MRI dataset. Through rigorous experimentation, our results highlight the superior diagnostic capabilities of the FViT model with an accuracy (ACC) of 99.68% and an F1-score (FS) of 99.68% for BT classification (using training-validation set). This result is also verified with high ACC of 99.31% and FS of 99.31%using testing set. The study demonstrates how well FViT model performs in classifying BT accurately, showcasing its potential in clinical settings. These findings emphasize the critical role of fine-tuning LVM models in advancing BT diagnosis through brain MRI images.

关键词： Deep learning Accuracy Magnetic resonance imaging Brain tumors Brain modeling Transformers Robustness Long short term memory Tuning Testing

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Revolutionizing Brain Tumor Diagnosis with Convolutional Neural Networks

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Procedia computer science 2025年 259卷 562-571页

作者： Aryan Singh Mohd Shuaib Manoj Diwakar Aditya Joshi Prabhishek Singh Neha Pandey Department of Computer Application Graphic Era Deemed to be University Dehradun 248001 India Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun 248001 India School of Computer Science Engineering and Technology Bennett University Greater Noida 201310 India Department of Biotechnology Graphic Era Deemed to be University Dehradun 248001 India

Historically, the diagnosis of brain tumors from magnetic resonance imaging (MRI) images has been a careful and extended endeavor that relies heavily on the expertise of radiologists and neurologists. In addition to being time-consuming, the complex nature of brain image analysis makes it vulnerable to cognitive biases and human error, which can affect the precision and effectiveness of diagnosis. Moreover, manual MRI scan interpretation might result in discrepancies between practitioners, which makes diagnosis even more difficult. These difficulties highlight the need for innovative approaches to improve the accuracy and speed of brain tumor identification as the need for quicker and more accurate medical evaluations keeps rising. To meet these difficulties, an innovative method based on image-driven Convolutional Neural Networks (CNNs) has been developed. Meningiomas, pituitary, gliomas, and absence tumors are the four major brain cancer groups that this research focuses on. With the basis of the EfficientNetB3 framework which had been pre-trained on the large-scale ImageNet dataset, the model uses a transfer learning method using a brain tumor dataset of 7023 images by adding extra layers that are exclusively meant to enhance classification and improved performance. The proposed method secures a notable accuracy of 99.84% and a precision of 99.33%. This advanced approach not only improves diagnostic precision but also significantly reduces the time required for neurologists to examine scans, enabling them to focus more on patient care and the development of preventive strategies. The efficiency of the proposed model underlines that it can become a breakthrough in clinical diagnostics and stimulate the progress of medical disciplines’ development. For a comprehensive overview of the model architecture and implementation, please visit the GitHub repository https://***/Aryansin1234/*** . Additionally, a live demonstration of

关键词： Brain Tumor classification Deep Learning Convolutional Neural Network Transfer Learning Magnetic Resonance Imaging

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Predicting Lumbar Spondylolisthesis: A Hybrid Deep Learning Approach

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Intelligent Automation & Soft Computing 2023年第8期37卷 2133-2151页

作者： Deepika Saravagi Shweta Agrawal Manisha Saravagi Sanjiv K.Jain Bhisham Sharma Abolfazl Mehbodniya Subrata Chowdhury Julian L.Webber Department of Computer Application SAGE UniversityIndore452012Madhya PradeshIndia IAC SAGE UniversityIndore452012Madhya PradeshIndia Shivang College of Physiotherapy Ratlam4570021Madhya PradeshIndia Medi-Caps University Rau IndoreMadhya Pradesh453331India Chitkara University Institute of Engineering and Technology Chitkara UniversityPunjabIndia Department of Electronics and Communication Engineering Kuwait College of Science and Technology(KCST)DohaKuwait Department of Computer Science and Engineering Sreenivasa Institute of Technology and Management StudiesChittoorAndra PradeshIndia

Spondylolisthesis is a chronic disease,and a timely diagnosis of it may help in avoiding *** identification in x-ray radiographs is very *** the feature extraction tool in VGG16 has improved the classification *** the fully connected layers of VGG16 are not efficient at capturing the positional structure of an object in *** network(CapsNet)works with capsules(neuron clusters)rather than a single neuron to grasp the properties of the provided image to match the *** this study,an integrated model that is a combination of VGG16 and CapsNet(S-VCNet)is *** the model,VGG16 is used as a feature *** feature extraction,the output is fed to CapsNet for disease identification.A private dataset is used that contains 466 X-ray radiographs,including 186 images displaying a spine with spondylolisthesis and 280 images depicting a normal *** suggested model is the first step towards developing a web-based radiological diagnosis tool that can be utilized in outpatient clinics where there are not enough qualified medical *** results demonstrate that the developed model outperformed the other models that are used for lumbar spondylolisthesis diagnosis with 98%*** the performance check,the model has been successfully deployed on the Gradio web app platform to produce the outcome in less than 20 s.

关键词： Gradio lumbar spondylolisthesis transfer learning VGG16 machine learning deep learning

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Time Management Recommendations with Bayesian Neural Networks: An Intelligent Assistant Approach

Time Management Recommendations with Bayesian Neural Network...

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2024 IEEE International Conference on Communication, Computing and Signal Processing, IICCCS 2024

作者： Kumar, Amit Soni, Nidhi Abduraximovich, Sulaymonov Dilshod Al-Jawahry, Hassan M. Jayasree, S. Rajaram, Gnanajeyaraman Chandigarh Group of Colleges Chandigarh Engineering College Department of Computer Application Punjab Mohali India Ies University Ies Institute of Technology and Management Department of Computer Science & Engineering M.P. Bhopal India Fergana Medical Institute of Public Health Department of Urology and Oncology Fergana Uzbekistan The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq The Islamic University of Al Diwaniyah College of Technical Engineering Department of Computers Techniques Engineering Al Diwaniyah Iraq Prince Shri Venkateshwara Padmavathy Engineering College Chennai India Saveetha Institute of Medical and Technical Sciences Simats Saveetha School of Engineering Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350390759

Time management is an important aspect of our daily lives and this research aims at creating an intelligent assistant that will be able to support an individual in managing their time appropriately using application of the Bayesian Neural Networks (BNNs). Many of the conventional time management strategies are posed to be ineffective since they cannot be changed depending on the unique person and situations in a similar way personal organization systems cannot be changed to suit the unique person and situations. Therefore, the use of Bayesian neural networks (BNNs) to perform inference makes them ideal for this purpose given that BNNs use probability and Bayesian inference to work under uncertainty. The involved process involved utilization of effective data gathering methodologies to obtain comprehensive information on users' activities, preferences, and productivity to develop the BNN model. The plan is that the latter was integrated into the context of a mobile application where, similar to an intelligent agent of an organism, it adapts in real time and suggests the most efficient ways of planning the time. A number of user tests pointed at level 3: Most of the users reported high levels of satisfaction and ranked productivity gains throughout the different activities under study. Escalating accuracy was also affirmed by the result analyses owing to the continual refining of the model in subsequent phases. This work demonstrates the possibility of the proposed BNN approach to change time management approaches and present a more effective approach of time management in line with the need of the user. As such, future work will comprise a continuation of the model's development with the addition of new functions that will complement the existing features and make the tool even more useful and appealing to potential users. © 2024 IEEE.

关键词： bayesian neural network intelligence assistance approach neural network time management

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ChatGPT Based Image Steganography (CGIS): A Novel Intelligent Information Hiding Approach to Achieve Secure Covert Communication

ChatGPT Based Image Steganography (CGIS): A Novel Intelligen...

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Advances in Electrical, Electronics and Computational Intelligence (ICAEECI), International Conference on

作者： Subhadip Mukherjee Somnath Mukhopadhyay Sunita Sarkar Department of Computer Science and Engineering Assam University Silchar Department of Computer Application Sikkim University Gangtok India

Covert communication refers to the practice of exchanging information or messages in a discreet or secretive manner, with the intent of keeping the communication hidden from unintended or unauthorized recipients. Steganography is a widely used, strategy for establishing covert communication, for maintaining the privacy and security of sensitive information. Steganography ensures that the message remains concealed from surveillance or eavesdropping. Artificial Intelligence (AI) plays a crucial role in enhancing security across various domains of covert communication. ChatGPT is an AI model, specifically a natural language processing (NLP) model. It belongs to the broader category of AI models which can comprehend and produce language for humans. In this paper, a new image steganographic model named CGIS is developed for covert communication using ChatGPT. Proposed steganographic method has achieved 3.0 bpp of hiding capacity. In situations where confidentiality is critical, such as in military operations, intelligence agencies, or corporate strategies, proposed method ensures that the message cannot be detected and or extracted by any surveillance or eavesdropper.

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NOFTrack: Efficient Multi-Object Tracking with a Speed Advantage

NOFTrack: Efficient Multi-Object Tracking with a Speed Advan...

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2024 International Symposium on Digital Home, ISDH 2024

作者： Jiang, Ming Ke, Jiehao Guo, Biao Metaverse Application Engineering Center Guilin University of Electronic Technology Guilin China School of Computer Science and Information Security Guilin University of Electronic Technology Guilin China Guilin University of Electronic Technology Guangxi Key Laboratory of Trusted Software Guilin China

ISBN: (纸本)9798331509873

In the field of multi-object tracking (MOT), accurately tracking the position and characteristics of targets is crucial. However, traditional methods often overlook detection results with low confidence, which can lead to the omission of targets and the fragmentation of trajectories. To overcome these challenges, we have introduced a novel NOF algorithm. This algorithm efficiently handles detection boxes through a tiered strategy, initially using an adaptive Kalman filter for preliminary matching, followed by a secondary screening of detection boxes with lower confidence, and a final stage for processing dubious detection results. This approach not only enhances the speed and accuracy of tracking but also performs exceptionally well when dealing with complex scenes. Moreover, it is capable of achieving high efficiency, with a rate of over 300 frames per second (FPS), ensuring the efficiency and precision of the tracking process. © 2024 IEEE.

关键词： Target tracking

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FViT: Finetuned Vision Transformer for Improved Brain Tumor Classification

FViT: Finetuned Vision Transformer for Improved Brain Tumor ...

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2025 International Conference on Ambient Intelligence in Health Care, ICAIHC 2025

作者： Chandra, Lalini Chandra, Tej Bahadur Rathy, Anirud Rai, Anushka National Institute of Technology Department of Computer Application Chhattisgarh Raipur India Bennett University School of Computer Science Engineering and Technology UP Greater Noida India

ISBN: (纸本)9798331530259

Timely detection and treatment of brain tumors (BT) remain a critical challenge due to their rapid growth and complex morphologies. Recent deep learning (DL) approaches and large vision models (LVMs) have promising potential in early and accurate diagnosis of BTs. However, the optimal tuning of hyperparameters is crucial for increasing the model's efficacy and robustness. Large vision models have significant potential for improving the accuracy of diagnosing BT using MRI images. Additionally, we evaluated and compared the performance of recent DL models ResNet50, VGG16, and Long Short-Term Memory (LSTM) using BT MRI dataset. Through rigorous experimentation, our results highlight the superior diagnostic capabilities of the FViT model with an accuracy (ACC) of 99.68% and an F1-score (FS) of 99.68% for BT classification (using training-validation set). This result is also verified with high ACC of 99.31% and FS of 99.31%using testing set. The study demonstrates how well FViT model performs in classifying BT accurately, showcasing its potential in clinical settings. These findings emphasize the critical role of fine-tuning LVM models in advancing BT diagnosis through brain MRI images. © 2025 IEEE.

关键词： Diagnostic radiography

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A new spaceborne/airborne bistatic synthetic aperture radar time synchronization method based on fractional Fourier transform 5

A new spaceborne/airborne bistatic synthetic aperture radar ...

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2023 5th International Conference on Information science, Electrical, and Automation engineering, ISEAE 2023

作者： Zhou, Fang Gui, Changchun Hu, Xinxiao Xing, Mengdao Zhang, Jiajia School of Computer Science and Information Engineering Hefei University of Technology Hefei230601 China Institute of Electronic Engineering Xidian University Xi'an710071 China Key Laboratory of Aperture Array and Space Application East China Research Institute of Electronic Engineering Hefei230088 China

ISBN: (纸本)9781510667440

With the change of observation demands, traditional monostatic synthetic aperture radar(SAR) can no longer meet the requirements such as passive reception and cooperative multimode imaging. Spaceborne/Airborne hybrid bistatic SAR(SA-BSAR) emerges as the times require, and has been widely concerned with its flexibility, concealment. However, as the receiver and transmitter do not share the same frequency oscillation source, the impact of time error on imaging cannot be ignored as the satellite operates for a long time. At the same time, SA-BSAR is in the form of double root sign in the course of slant range. How to obtain two-dimensional(2-D) spectrum is also the key to imaging. This paper proposes a new method to estimate time error based on fractional Fourier transform (FRFT), which simplifies the traditional time error estimation steps. Finally, the Air-Phase method is used to solve the 2-D spectrum to achieve the target imaging. The algorithms proposed in this paper are implemented by FFT without interpolation, which ensures the imaging efficiency. The final experimental results show the effectiveness of the algorithm. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Synthetic aperture radar

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