Coronavirus Diagnosis Based on Chest X-Ray Images and Pre-Trained DenseNet-121

Kateb, Yousra; Meglouli, H.; Khebli, Abdelmalek

doi:10.18280/ria.370104

Cited by 7 publications

(4 citation statements)

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“…The outputs of these 1000 categories were the results of these frozen fully-connected layers, which needed the use of the two-phase transfer learning approach. A new fully-connected layer, a SoftMax layer, and an output layer for four-class classification were required to replace them [27][28][29][30][31][32][33]…”

Section: Figure 4 Basic Architecture Of Proposed Methodologymentioning

confidence: 99%

Enhanced Classification of Alzheimer’s Disease Stages via Weighted Optimized Deep Neural Networks and MRI Image Analysis

Aparna,

Rao

2023

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Alzheimer's disease, a debilitating neurological disorder, precipitates irreversible cognitive decline and memory loss, predominantly affecting individuals aged 65 years and above. The need for an automated system capable of accurately diagnosing and stratifying Alzheimer's disease into distinct stages is paramount for early intervention and management. However, existing deep learning methodologies are often hampered by protracted training times. In this study, a time-efficient approach incorporating a two-phase transfer learning technique is proposed to surmount this challenge. This method is particularly efficacious in the analysis of Magnetic Resonance Imaging (MRI) data for the identification of Alzheimer's disease. The proposed detection system employs two-phase transfer learning, augmented with fine-tuning for multi-class classification of brain MRI scans. This allows for the categorization of images into four distinct classes: Mild Dementia (MD), Moderate Dementia (MOD), Non-Dementia (ND), and Very Mild Dementia (VMD). The classification of Alzheimer's disease was conducted using various pre-trained deep learning models, including ResNet50V2, InceptionResNetV2, Xception, DenseNet121, VGG16, and MobileNetV2. Among the models tested, ResNet50V2 demonstrated superior performance, achieving a training classification accuracy of 99.35% and a testing accuracy of 99.25%. The results underscore the potential of the proposed method in delivering more accurate classifications than those obtained from extant models, thereby contributing to the early detection and stratification of Alzheimer's disease.

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Section: Figure 4 Basic Architecture Of Proposed Methodologymentioning

confidence: 99%

Enhanced Classification of Alzheimer’s Disease Stages via Weighted Optimized Deep Neural Networks and MRI Image Analysis

Aparna,

Rao

2023

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“…Deep learning-based techniques, notably convolutional neural networks (CNN), have experienced great success in image classification tasks in recent years [19,20]. CNN has great characterization capabilities [21,22] and is very successful at recognizing strip surface flaws [6,8,17].…”

Section: Related Workmentioning

confidence: 99%

Classification of Surface Defects in Steel Sheets Using Developed NasNet-Mobile CNN and Few Samples

Kateb,

Khebli,

Meglouli

2024

RIA

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Rolled steel is a major product of ferrous metalworking. It is a popular metal structure construction technology. Though a big amount of the finished product may be flawed, the process of manufacturing must be improved. It is critical to correctly classify hot-rolled strip faults. As a result, in recent years, numerous machine-learning-based automated visual inspection (AVI) systems have been created. However, these approaches lack several critical components, such as insufficient RAM, which causes complexity and slowness during implementation. Long execution durations, in general, cause the process to be delayed or completed later than expected. A shortage of faulty samples is also a significant difficulty in steel defect detection, as the imbalance between the huge number of nondefective photos and the defective ones causes the algorithm to be unfair in categorization. To address these three issues, a deep CNN model is created in this study. The backbone architecture is a pre-trained NasNet-Mobile that has been fine-tuned with particular parameters to be compatible with the required data. Despite having 27 times less data than other articles' datasets, the model detects steel surface photos with six defects with 99.51% accuracy, exceeding earlier methodologies. This study is useful for surface fault classification when the sample size is small, the software is not quite as effective, or time is limited. Avoiding these issues will help the steel industry improve safety and end product quality while also saving time and money.

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“…CNNs are used for classification and prediction in computer vision tasks. For example, the LeNet-5 [46,47], the AlexNet [48], the VGG-16 [49,50], the DenseNet121 [51,52], the ResNet50 [53,54], and the MobileNet-V2 [55,56] have been used for classification tasks, while the U-Net [57,58] has been used for semantic segmentation problems. At present, some challenges addressed by computer vision [59] through DL are (a) image and video synthesis to create realistic images and videos for content creation and entertainment, (b) image style transfer to merge the artistic style of one image with the content of another, (c) text-to-image synthesis to extract meaning from the text description and convert it into an image for image editing, (d) enhancing the capabilities of autonomous vehicles to more precisely handle difficult driving scenarios, (e) detecting early signs of diseases before the symptoms appear, (f) identifying suspicious behavior or objects more accurately for security purposes, and (g) making DL models more interpretable, especially in applications where human lives, safety, and ethics are involved.…”

Section: Literature Reviewmentioning

confidence: 99%

Demystifying Deep Learning Building Blocks

Ochoa Domínguez,

Cruz Sánchez,

Vergara Villegas

2024

Mathematics

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Building deep learning models proposed by third parties can become a simple task when specialized libraries are used. However, much mystery still surrounds the design of new models or the modification of existing ones. These tasks require in-depth knowledge of the different components or building blocks and their dimensions. This information is limited and broken up in different literature. In this article, we collect and explain the building blocks used to design deep learning models in depth, starting from the artificial neuron to the concepts involved in building deep neural networks. Furthermore, the implementation of each building block is exemplified using the Keras library.

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Coronavirus Diagnosis Based on Chest X-Ray Images and Pre-Trained DenseNet-121

Cited by 7 publications

References 0 publications

Enhanced Classification of Alzheimer’s Disease Stages via Weighted Optimized Deep Neural Networks and MRI Image Analysis

Enhanced Classification of Alzheimer’s Disease Stages via Weighted Optimized Deep Neural Networks and MRI Image Analysis

Classification of Surface Defects in Steel Sheets Using Developed NasNet-Mobile CNN and Few Samples

Demystifying Deep Learning Building Blocks

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