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Utilizing Correlation Analysis for Validation of image Datasets in Lower Spine Diagnosis: A Study on MRI image Dataset from Multiple Labs in Punjab

Utilizing Correlation Analysis for Validation of Image Datas...

引用

Blockchain and Distributed Systems Security (ICBDS), ieee international conference on

作者： Ruchi Dalwinder Singh Manik Rakhra Arun Singh Department of Computer Science & Engineering Lovely Professional University Jalandhar Phagwara India

For the purposes of making a diagnosis of a condition affecting the lower spine, the collection of data is an obligatory requirement. It's possible that the data that was obtained was in the form of text, but it may also have been an image. The gathering of data in the work that is being suggested is, to a primary extent, dependent upon picture datasets. MRI image dataset compiled by travelling to a number of different labs in Punjab (Pathankot) and making use of the internet as a method of gathering. The utilisation of correlation analysis is what enables the validation of datasets to become a reality. The data that has been obtained has inconsistencies that need to be fixed in order to be useful. The pre-processing mechanism that comprises edge detection and contrast stretching is also illustrated through the use of this proposed study's use of the procedure, along with the result of employing the technique. A comparison of the outcomes achieved by utilising methods such as histogram equivalence and slicing in conjunction with contrast stretching gives the largest possible result of contrast stretching. This conclusion was reached after doing the comparison. Only 3032 of the dataset's 5000 samples exhibit a positive connection; hence, only those samples were maintained to indicate decreased spine detection. The dataset contains a total of 5000 samples. The dataset contains samples that were collected from a wide range of laboratories.

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A Dual Approach to image and Video Inpainting using 3D-Edge-Curvelet Transform and CEGAN Architecture

A Dual Approach to Image and Video Inpainting using 3D-Edge-...

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international conference on Recent Trends in Advance Computing (ICRTAC)

作者： Saimirra R Ankith Motha Manik Suthar Ezekiel Jebaraj Solomon Vedhapriyavadhana R School of Computer Science and Engineering Chandigarh University Chandigarh India Department of Computer Applications Vellore Institute of Technology (VIT) Chennai India

This research paper presents innovative methods for both video and image inpainting, addressing the removal of moving foreground and background objects in videos as well as the restoration of corrupted or missing regions in images. The video inpainting method involves a sequence of key stages, including Pre-processing, Motion Inpainting, and Background Inpainting, with a crucial step being image Transformation during Pre-processing. The novel '3D-Edge-Curvelet Transform' is introduced for video inpainting, implemented using MATLAB 2013a. Evaluation is conducted on three videos (beach, crossing ladies, and jumping girl) based on six frames selected to showcase substantial motion in both foreground and background. Remarkable results, in terms of CPU Computation Time and Peak Signal to Noise Ratio (PSNR), are observed in frames 37 (beach), frame 59 (crossing ladies), and frame 109 (jumping girl).In addition, a distinct image inpainting method is introduced, which utilizes Context Encoding Generative Adversarial Networks (CEGANs) along with an innovative loss function to guarantee the perceptual and semantic consistency of in painted images. This approach incorporates a multi-scale framework to effectively address images with missing regions of different sizes. Empirical findings demonstrate its exceptional performance compared to other GAN-based image inpainting methods and established transformation techniques in Mathematics and Machine Learning.

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GAN, CNN and ELM Based Breast Cancer Detection

GAN, CNN and ELM Based Breast Cancer Detection

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Innovation in Technology (INOCON), ieee international conference for

作者： Vandana Kate R Ushasree R.M. Tharsanee Mrs. Taranjeet Kukreja Swarnlata Saraf Bhoopathy Varadharajan Acropolis Institute of Technology and Research Indore Madhya Pradesh India Dayananda Sagar Academy of Technology and Management Bangalore Karnataka India Department of Computer Science and Engineering Bannari Amman Institute of Technology Sathyamangalam Erode Tamilnadu India University Institute of Pharmacy Pandit Ravishankar Shukla University Raipur Chhattisgarh India Department of Computer Science and Engineering Malla Reddy College of Engineering Secunderabad Telangana India

The most prevalent kind of cancer among females is known as breast cancer. One of the most significant challenges facing modern medicine is improving the accuracy of breast cancer screenings. In addition to lowering costs associated with treatment, accurate diagnosis of cancer, regardless of whether it is benign or malignant, is of utmost importance. image processing is a tool that can detect breast cancer in its early stages without causing any damage. The processing of images comes with a number of inherent uncertainties that can be caused by a variety of factors, including the sampling to noise ratio, the initial digitalization, the intensity, and the special domain. We put the Extreme Learning Machine (ELM) to work classifying, identifying, and instructing. This research suggests a GAN-CNN-ELM-based technique for detecting breast cancer. This method produces an accuracy of about 99% which outperforms the GAN-CNN model.

关键词： Technological innovation Uncertainty Costs Extreme learning machines image processing Generative adversarial networks Breast cancer

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LSSED: A Robust Segmentation Network for Inflamed Appendix from CT images

LSSED: A Robust Segmentation Network for Inflamed Appendix f...

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international conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Wing W Y. Ng Peixin Zheng Ting Wang Jianjun Zhang Yinhao Liang Hui Zhou Dan Liang Guangming Li Xinhua Wei School of Computer Science and Engineering South China University of Technology Guangzhou China Department of Radiology Guangzhou First People’s Hospital School of Medicine South China University of Technology Guangzhou China Department of Radiology The Sixth Affiliated Hospital of Guangzhou Medical University Qingyuan China

Acute appendicitis (AA) is one of the most prevalent surgical acute abdominal condition diseases. The treatment management of A A is highly dependent on the CT image diagnosis. However, the in-flamed appendix exhibits blurred boundaries with nearby tissue, varying shapes, and sizes. These properties require high robustness and generalization capability of inflamed appendix segmentation networks. In this paper, we propose a CNN-Transformer-based encoder-decoder segmentation network (LSSED) equipped with localized stochastic sensitivity (LSS) loss function and residual dilated paths (RD-Paths) to solve above problems. The proposed method effectively learns robust features of the input data by reducing the LSS of unseen samples. In addition, the RD-Paths capture multiscale feature information and reduce the semantic gap between the encoder and decoder, which improves the accuracy of the segmentation. Empirical studies on a real-world AA dataset show that our method yields the best performance in terms of average Dice similarity coefficient (DSC) and Hausdorff Distance of 95% (HD95) compared to several state-of-the-art segmentation networks.

关键词： Training image segmentation Sensitivity Shape Computed tomography Semantics Surgery

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RawHDR: High Dynamic Range image Reconstruction from a Single Raw image

RawHDR: High Dynamic Range Image Reconstruction from a Singl...

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international conference on computer Vision (ICCV)

作者： Yunhao Zou Chenggang Yan Ying Fu Beijing Institute of Technology Hangzhou Dianzi University

High dynamic range (HDR) images capture much more intensity levels than standard ones. Current methods predominantly generate HDR images from 8-bit low dynamic range (LDR) sRGB images that have been degraded by the camera processing pipeline. However, it becomes a formidable task to retrieve extremely high dynamic range scenes from such limited bit-depth data. Unlike existing methods, the core idea of this work is to incorporate more informative Raw sensor data to generate HDR images, aiming to recover scene information in hard regions (the darkest and brightest areas of an HDR scene). To this end, we propose a model tailor-made for Raw images, harnessing the unique features of Raw data to facilitate the Raw-to-HDR mapping. Specifically, we learn exposure masks to separate the hard and easy regions of a high dynamic scene. Then, we introduce two important guidances, dual intensity guidance, which guides less informative channels with more informative ones, and global spatial guidance, which extrapolates scene specifics over an extended spatial domain. To verify our Raw-to-HDR approach, we collect a large Raw/HDR paired dataset for both training and testing. Our empirical evaluations validate the superiority of the proposed Raw-to-HDR reconstruction model, as well as our newly captured dataset in the experiments.

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Resource-Limited Localized Adaptive Adversarial Training for Machine Learning Model

Resource-Limited Localized Adaptive Adversarial Training for...

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ieee international conference on Big Data and Cloud Computing (BdCloud)

作者： Mohammed Rajhi Niki Pissinou School of Computing and Information Sciences Florida International University Miami FL USA Faculty of Computer Science & Information Technology Jazan University Jazan Saudi Arabia

Balancing robustness and computational efficiency in machine learning models is challenging, especially in settings with limited resources like mobile and IoT devices. This study introduces Adaptive and Localized Adversarial Training (ALAT), an optimization approach that balances these competing needs. ALAT combines generalized models with localized adversarial perturbations and adaptive data augmentation. As a result, the model strengthens its weak points without needing to explore all possible adversarial threats, saving computational effort Our data shows that ALAT-trained models perform robustly with less computational cost compared to traditional adversarial training methods. This adaptability makes ALAT suitable for various machine learning architectures and particularly valuable in resource-constrained settings requiring resilience to adversarial threats.

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CO-PILOT: Dynamic Top-Down Point Cloud with Conditional Neighborhood Aggregation for Multi-Gigapixel Histopathology image Representation

CO-PILOT: Dynamic Top-Down Point Cloud with Conditional Neig...

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international conference on computer Vision (ICCV)

作者： Ramin Nakhli Allen Zhang Ali Mirabadi Katherine Rich Maryam Asadi Blake Gilks Hossein Farahani Ali Bashashati University of British Columbia

Predicting survival rates based on multi-gigapixel histopathology images is one of the most challenging tasks in digital pathology. Due to the computational complexities, Multiple Instance Learning (MIL) has become the conventional approach for this process as it breaks the image into smaller patches. However, this technique fails to account for the individual cells present in each patch, while they are the fundamental part of the tissue. In this work, we developed a novel dynamic and hierarchical point-cloud-based method (CO-PILOT) for the processing of cellular graphs extracted from routine histopathology images. By using bottom-up information propagation and top-down conditional attention, our model gains access to an adaptive focus across different levels of tissue hierarchy. Through comprehensive experiments, we demonstrate that our model can outperform all the state-of-the-art methods in survival prediction, including the hierarchical Vision Transformer (ViT), across three datasets and four metrics with only half of the parameters of the closest baseline. Importantly, our model is able to stratify the patients into different risk cohorts with statistically different outcomes across three large datasets, a task that was previously achievable only using genomic information. Furthermore, we publish a large dataset containing 873 cellular graphs from 188 patients, along with their survival information, making it one of the largest publicly available datasets in this context.

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Plug-and-Play with POA based Maximum a Posteriori Denoisers for image

Plug-and-Play with POA based Maximum a Posteriori Denoisers ...

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Mysore Sub Section international conference (MysuruCon), ieee

作者： Adithya Padthe Sapna Kadakadiyavar Ramya Thatikonda Madhura G K Department of Information Technology University of the Cumberlands USA AI &ML Department Vemana Institute of Technology Kormangala Bangalore PhD in Information Technology at University of the Cumberlands AI &ML Department Nitte Meenakshi Institute of Technology Bengaluru

Restoring damaged images has been an issue in the fields of image processing and basic computer vision for decades. Due to their improved performance, techniques based on discriminative convolutional neural networks (CNNs) have recently garnered a lot of interest. However, most of these frameworks only operate well on one type of picture restoration assignment, hence their performance on other types of image restoration is usually subpar. Research techniques leverage the fact that the Maximum a Posteriori (MAP) optimisation may be broken down into smaller sub-problems, such as a MAP denoising optimisation, to resolve this issue. We introduce the first full-stack method for MAP estimation in deep neural network image denoising. We prove that our approach will always result in a smaller MAP denoising goal, which can subsequently be employed in the general picture restoration optimisation technique known as the Puzzle Optimisation technique (POA). Our technique is theoretically analysed, and its quantitative efficacy is demonstrated through a number of tests. Our experiments validate the theoretical foundations of MAP and demonstrate that the suggested approach can reach 70x quicker presentation than the state-of-the-art.

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Analytical Insight into Cutting-Edge image Captioning for Advanced ChatGPT Functionality 2nd

Analytical Insight into Cutting-Edge Image Captioning for Ad...

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2nd international conference on Business Data Analytics, ICBDA 2023

作者： Manisha Kumar, Avadhesh Yadav, Dileep Kumar School of Computer Science Engineering Galgotias University Greater Noida India SCSET Bennett University Greater Noida India

ISBN: (纸本)9783031807770

At present, Artificial intelligence (AI) and Machine learning (ML) algorithm contribute to transforming the technology environment. Specifically, the development of chatbots has been on a rapid-fire overhead in recent years. The image captioning with ChatGPT is most growing field for text generating for the future. In recent years, the innovation of virtual assistants has boomed with ChatGPT becoming an increasingly popular language model of artificial intelligence. This work evaluates the principles, goals, and problems of ChatGPT in the present study. This study explores the technology's background, growth, and development, along with its applications. In addition, we will look at the benefits of connecting information by ChatGPT in image processing and content writing that the image exposes. However, we believe that ChatGPT will contribute to improving human-AI interaction and bridging scientific gaps by focusing on various research and development opportunities, such as energy conservation, privacy, and security, expanding its scope to other technologies like robotics and computer vision, deep learning, and ML. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： ChatGPT model computer vision NLP

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A Review on Heartbeat Classification for Arrhythmia Detection Using ECG signal processing

A Review on Heartbeat Classification for Arrhythmia Detectio...

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ieee Students' conference on Electrical, Electronics and computer Science (SCEECS)

作者： Praveen Kumar Tyagi Neha Rathore Dheeraj Agrawal Department of ECE Maulana Azad National Institute of Techonology Bhopal India

The electrocardiogram (ECG) provides essential characteristics of the human heart's multiple cardiac conditions. The classification of arrhythmias provides a major part in the diagnosis of cardiac disease. Any deviation from the normal sequence of electrical impulses is considered an arrhythmia. Traditional methods of signal processing, machine learning and its sub-branches, such as deep learning, are popular techniques for ECG signal analysis and classification and, above all, for the development of early detection and treatment applications for cardiac conditions and arrhythmias. This article presents a detailed literature survey on ECG signal analysis. This paper aims to analyze the most recent studies on data utilized, features, and machine learning approaches that can address the time computational challenge and be implemented in wearable technology. The study methodology began with a search for relevant papers, followed by a study of the data provided. The second stage was to explore the evaluated ECG characteristics and the machine learning method used to identify arrhythmia. According to the analysis, a significant number of studies selected the MIT-BIH database, even though it needs a substantial ratio of pre-processing effort. We address a detailed existing research work review on the data of real-time signal collection, pre-recorded diagnostic ECG data, analysis and denoising of ECG signals, identification of ECG spectrographic states based upon function technologies, and classification of ECG signals, as well as comparative discussions between the studies analyzed.

关键词： Training Arrhythmia Wearable computers Noise reduction Cardiac disease Electrocardiography Real-time systems

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