Explainable AI for Transparent MRI Segmentation: Deep Learning and Visual Attribution in Clinical Decision Support

The digital imaging technique known as Computed Radiography (CR) has transformed the medical imaging industry by providing a number of advantages. It eliminates the need for traditional film-based methods, making it more efficient and convenient. A common issue faced with CR images is the presence of grid artifacts and other pattern artifacts, which can have a significant impact on the quality of the images when viewed on a computer screen, especially if a clinic-grade display is not accessible. This paper presents a novel framework for removing grid line artifacts from X-ray images, which is a critical challenge in medical imaging. The framework proposes a hybrid Deep Grid model that combines a Gaussian band-stop filter with ADAM optimization to produce high-quality, grid-line free X-ray images that are suitable for further analysis and diagnosis. Deep learning (DL) models for instance the Convolutional Neural Network (CNN), DenseNet, VGG-Net, and Fast R-CNN were utilized to classify images, and the grid-by-grid removal of grid lines in the image was performed. The proposed framework achieved a high accuracy rate of 98% in eliminating grid line artifacts from X-ray images, demonstrating its possibility for a big improvement the accuracy and reliability of diagnostics for medical based on X-ray images

Hybrid Deep Learning Based Model for Removing Grid-Line Artifacts from Radiographical Images

U. S. Pavitha,

S. Nikhila,

Mohan

2024

Adaptive Transformer-Based Multi-Modal Image Fusion for Real-Time Medical Diagnosis and Object Detection

R. Dineshkumar,

A. Ameelia Roseline,

Tatiraju V. Rajani Kanth

et al. 2024

In recent years, medical diagnosis and object detection have been significantly enhanced by the integration of multi-modal image fusion techniques. This study proposes an Adaptive Transformer-Based Multi-Modal Image Fusion (AT-MMIF) framework designed for real-time medical diagnosis and object detection. The framework employs a Transformer architecture to capture both global and local feature correlations across multiple imaging modalities, including MRI, CT, PET, and X-ray, for more accurate diagnostic results and faster object detection in medical imagery. The fusion process incorporates spatial and frequency-domain information to improve the clarity and detail of the output images, enhancing diagnostic accuracy. The adaptive attention mechanism within the Transformer dynamically adjusts to the relevant features of different image types, optimizing fusion in real time. This leads to an improved sensitivity (98.5%) and specificity (96.7%) in medical diagnosis. Additionally, the model significantly reduces false positives and negatives, with an F1 score of 97.2% in object detection tasks. The AT-MMIF framework is further optimized for real-time processing with an average inference time of 120 ms per image and a model size reduction of 35% compared to existing multi-modal fusion models. By leveraging the strengths of Transformer architectures and adaptive learning, the proposed framework offers a highly efficient and scalable solution for real-time medical diagnosis and object detection in various clinical settings, including radiology, oncology, and pathology.

HARGAN: Generative Adversarial Network BasedDeep Learning Framework for Efficient Recognition of Human Actions from Surveillance Videos

Boddupally JANAIAH,

Suresh PABBOJU

2024

Analyzing public surveillance videos has become an important research area as it is linked to different real-world applications. Video Analytics for human action recognition is given significance due to its utility. However, it is very challenging to analyze live-streaming videos to identify human actions across the frames in the video. The literature showed that Convolutional Neural Networks (CNNs) are among computer vision applications' most popular deep learning algorithms. Another important observation is that Generative Adversarial Network(GAN) architecture with deep learning has the potential to leverage effectiveness in applications using computer vision. Inspired by this finding, we created a GAN-based framework (called HARGAN) in this research for human activity identification from surveillance films. The framework exploits a retrained deep learning model known as ResNet50 and convolutional LSTM for better performance in action recognition. Our framework has two critical functionalities: feature learning and human action recognition. The ResNet50 model achieves the former, while the GAN-based convolutional LSTM model achieves the latter. We proposed an algorithm called the Generative Adversarial Approach for Human Action Recognition (GAA-HAR) to realize the framework. We used a benchmark dataset known as UCF50, which is extensively used in studies on human action identification. Based on our experimental findings, the suggested framework performs better than the current baseline models like CNN, LSTM, and convolutional LSTM, with the highest accuracy of 97.73%. Our framework can be used in video analytics applications linked to large-scale public surveillance.