arXiv subjects

Haytham Al Ewaidat

Publications and source records attributed to Haytham Al Ewaidat.

2 recordsLinked to original sources

arXiv2024

Frequency-Guided U-Net: Leveraging Attention Filter Gates and Fast Fourier Transformation for Enhanced Medical Image Segmentation

Purpose Medical imaging diagnosis faces challenges, including low-resolution images due to machine artifacts and patient movement. This paper presents the Frequency-Guided U-Net (GFNet), a novel approach for medical image segmentation that addresses challenges associated with low-resolution images and inefficient feature extraction. Approach In response to challenges related to computational cost and complexity in feature extraction, our approach introduces the Attention Filter Gate. Departing from traditional spatial domain learning, our model operates in the frequency domain using FFT. A strategically placed weighted learnable matrix filters feature, reducing computational costs. FFT is integrated between up-sampling and down-sampling, mitigating issues of throughput, latency, FLOP, and enhancing feature extraction. Results Experimental outcomes shed light on model performance. The Attention Filter Gate, a pivotal component of GFNet, achieves competitive segmentation accuracy (Mean Dice: 0.8366, Mean IoU: 0.7962). Comparatively, the Attention Gate model surpasses others, with a Mean Dice of 0.9107 and a Mean IoU of 0.8685. The widely-used U-Net baseline demonstrates satisfactory performance (Mean Dice: 0.8680, Mean IoU: 0.8268). Conclusion his work introduces GFNet as an efficient and accurate method for medical image segmentation. By leveraging the frequency domain and attention filter gates, GFNet addresses key challenges of information loss, computational cost, and feature extraction limitations. This novel approach offers potential advancements for computer-aided diagnosis and other healthcare applications. Keywords: Medical Segmentation, Neural Networks,

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arXiv2022

Identification of lung nodules CT scan using YOLOv5 based on convolution neural network

Purpose: The lung nodules localization in CT scan images is the most difficult task due to the complexity of the arbitrariness of shape, size, and texture of lung nodules. This is a challenge to be faced when coming to developing different solutions to improve detection systems. the deep learning approach showed promising results by using convolutional neural network (CNN), especially for image recognition and it's one of the most used algorithm in computer vision. Approach: we use (CNN) building blocks based on YOLOv5 (you only look once) to learn the features representations for nodule detection labels, in this paper, we introduce a method for detecting lung cancer localization. Chest X-rays and low-dose computed tomography are also possible screening methods, When it comes to recognizing nodules in radiography, computer-aided diagnostic (CAD) system based on (CNN) have demonstrated their worth. One-stage detector YOLOv5 trained on 280 annotated CT SCAN from a public dataset LIDC-IDRI based on segmented pulmonary nodules. Results: we analyze the predictions performance of the lung nodule locations, and demarcates the relevant CT scan regions. In lung nodule localization the accuracy is measured as mean average precision (mAP). the mAP takes into account how well the bounding boxes are fitting the labels as well as how accurate the predicted classes for those bounding boxes, the accuracy we got 92.27%. Conclusion: this study was to identify the nodule that were developing in the lungs of the participants. It was difficult to find information on lung nodules in medical literature.

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