COVID-19 Detection on X-Ray Images using a Combining Mechanism of Pre-trained CNNs

Gannour, Oussama El; Hamida, Soufiane; Saleh, Shawki; Lamalem, Yasser; Cherradi, Bouchaib; Raihani, Abdelhadi

doi:10.14569/ijacsa.2022.0130668

Cited by 14 publications

(6 citation statements)

References 31 publications

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“…This paper aims to provide a comprehensive analysis of proposed DL techniques for the classification of cutaneous diseases, with a particular focus on Transfer Learning (TL), Deep Neural Networks (DNN), and the innovative concept of multimodal classifiers. TL involves refining previously trained models for specific tasks using pre-trained models, while DNNs are multilayered architectures capable of learning complex data features and patterns [7]. Multimodal classifiers are a category of DL models that are skilled at processing various data modalities, including images, text, and numeric data, while simultaneously performing multiple tasks, such as classification and regression.…”

Section: An Integrated Multimodal Deep Learning Framework For Accurat...mentioning

confidence: 99%

An Integrated Multimodal Deep Learning Framework for Accurate Skin Disease Classification

Hamida,

Lamrani,

Bouqentar

et al. 2024

Int. J. Onl. Eng.

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In order to effectively treat skin diseases, an accurate and prompt diagnosis is required. In this article, a novel method for classifying skin disorders using a multimodal classifier is presented. The proposed classifier utilizes multiple information sources to enhance the accuracy of disease classification. It incorporates images of skin lesions and patient-specific data. The multimodal classifier simultaneously classifies diseases by combining image and structured data inputs. The effectiveness of the proposed classifier was evaluated using the ISIC 2018 dataset, which includes images and clinical data for seven categories of skin diseases. The results indicate that the proposed model outperforms conventional single-modal and single-task classifiers, achieving an accuracy of 98.66% for image classification and 94.40% for clinical data classification. In addition, we compare the performance of the proposed model with that of other methodologies, demonstrating its superiority. Despite yielding promising results, the proposed method has limitations in terms of data requirements and generalizability. Future research directions include incorporating additional information sources, investigating genetic data integration, and applying the method to various medical conditions. This study illustrates the potential of integrating multimodal techniques with transfer learning in deep neural networks to enhance the classification accuracy of cutaneous diseases.

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Section: An Integrated Multimodal Deep Learning Framework For Accurat...mentioning

confidence: 99%

An Integrated Multimodal Deep Learning Framework for Accurate Skin Disease Classification

Hamida,

Lamrani,

Bouqentar

et al. 2024

Int. J. Onl. Eng.

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“…The rows represent the actual classes and the columns represent the predicted classes. Each cell in the matrix represents the number of instances that belong to a particular actual class and a particular predicted class [52]. Based on the entries in the confusion matrix, several evaluation metrics can be computed to assess the performance of the classifier.…”

Section: Confusion Matrix and Evaluation Metricsmentioning

confidence: 99%

Improved Multiclass Brain Tumor Detection using Convolutional Neural Networks and Magnetic Resonance Imaging

Mahjoubi¹,

Hamida²,

Gannour³

et al. 2023

IJACSA

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Recently, Deep learning algorithms, particularly Convolutional Neural Networks (CNNs), have been applied extensively for image recognition and classification tasks, with successful results in the field of medicine, such as in medical image analysis. Radiologists have a hard time categorizing this lethal illness since brain tumors include a variety of tumor cells. Lately, methods based on computer-aided diagnostics claimed to employ magnetic resonance imaging to help with the diagnosis of brain cancers (MRI). Convolutional Neural Networks (CNNs) are often used in medical image analysis, including the detection of brain cancers. This effort was motivated by the difficulty that physicians have in appropriately detecting brain tumors, particularly when they are in the early stages of brain bleeding. This proposed model categorized the brain image into four distinct classes: (Normal, Glioma, Meningioma, and Pituitary). The proposed CNN networks reach 95% of recall, 95.44% accuracy and 95.36% of F1-score.

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“…By combining the RF and DNN algorithms, this innovative hybrid approach capitalizes on their synergistic benefits. The system strives for superior diagnostic precision and dependability while optimizing time and resource utilization [12]. In addition, the hybrid system seeks to reduce dermatologists' reliance on subjective visual inspection, thereby mitigating the inherent subjectivity and time-intensiveness of conventional diagnostic methods.…”

Section: Introductionmentioning

confidence: 99%

Toward enhanced skin disease classification using a hybrid RF-DNN system leveraging data balancing and augmentation techniques

Hamida,

Lamrani,

El Gannour

et al. 2024

Bulletin EEI

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Significant health concerns are associated with skin diseases, and accurate and timely diagnosis is essential for effective treatment and patient management. To improve the classification of cutaneous diseases, we propose a novel hybrid system that incorporates the strengths of random forest (RF) and deep neural network (DNN) algorithms. The system employs data augmentation and balancing techniques to enhance model performance and generalizability. The HAM10000 dataset of diverse dermatoscopic images is used for training and evaluation in this study. In the hybrid system proposed, the RF model provides an initial diagnosis based on patient-reported symptoms, while the DNN analyzes images of skin lesions, resulting in more precise and efficient diagnoses. Using hyper-parameter optimization, we fine-tune the system for optimal performance. The evaluation demonstrates the accuracy of the hybrid model, which achieves a classification accuracy of 96.8% overall. According to our findings, the hybrid system demonstrates exceptional efficacy in six of seven skin disease classes. Variations in sensitivity and reliance on data quality and quantity are however cited as limitations. Nevertheless, this hybrid system has the potential to revolutionize skin disease diagnosis and treatment.

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COVID-19 Detection on X-Ray Images using a Combining Mechanism of Pre-trained CNNs

Cited by 14 publications

References 31 publications

An Integrated Multimodal Deep Learning Framework for Accurate Skin Disease Classification

An Integrated Multimodal Deep Learning Framework for Accurate Skin Disease Classification

Improved Multiclass Brain Tumor Detection using Convolutional Neural Networks and Magnetic Resonance Imaging

Toward enhanced skin disease classification using a hybrid RF-DNN system leveraging data balancing and augmentation techniques

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