Chest Diseases Classification Using CXR and Deep Ensemble Learning

Nasser, Adnane Ait; Akhloufi, Moulay A.

doi:10.1145/3549555.3549581

Cited by 7 publications

(5 citation statements)

References 15 publications

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“…A CNN is a type of DL model widely used for image segmentation [24], image classification [25,26], and object detection [27], where the connection between neurons is inspired by the visual cortex of animals [28]. In a CNN's architecture, multiple layers are present, encompassing convolutional, pooling, and fully connected layers.…”

Section: Convolutional Neural Network Modelsmentioning

confidence: 99%

A Hybrid Deep Learning Architecture for Apple Foliar Disease Detection

Ait Nasser,

Akhloufi

2024

Computers

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Incorrectly diagnosing plant diseases can lead to various undesirable outcomes. This includes the potential for the misuse of unsuitable herbicides, resulting in harm to both plants and the environment. Examining plant diseases visually is a complex and challenging procedure that demands considerable time and resources. Moreover, it necessitates keen observational skills from agronomists and plant pathologists. Precise identification of plant diseases is crucial to enhance crop yields, ultimately guaranteeing the quality and quantity of production. The latest progress in deep learning (DL) models has demonstrated encouraging outcomes in the identification and classification of plant diseases. In the context of this study, we introduce a novel hybrid deep learning architecture named “CTPlantNet”. This architecture employs convolutional neural network (CNN) models and a vision transformer model to efficiently classify plant foliar diseases, contributing to the advancement of disease classification methods in the field of plant pathology research. This study utilizes two open-access datasets. The first one is the Plant Pathology 2020-FGVC-7 dataset, comprising a total of 3526 images depicting apple leaves and divided into four distinct classes: healthy, scab, rust, and multiple. The second dataset is Plant Pathology 2021-FGVC-8, containing 18,632 images classified into six categories: healthy, scab, rust, powdery mildew, frog eye spot, and complex. The proposed architecture demonstrated remarkable performance across both datasets, outperforming state-of-the-art models with an accuracy (ACC) of 98.28% for Plant Pathology 2020-FGVC-7 and 95.96% for Plant Pathology 2021-FGVC-8.

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Section: Convolutional Neural Network Modelsmentioning

confidence: 99%

A Hybrid Deep Learning Architecture for Apple Foliar Disease Detection

Ait Nasser,

Akhloufi

2024

Computers

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“…Ait Nasser and Akhloufi [64] used different data augmentation techniques, including a rotation of −15 to 15 degrees, a translation of 20% in four directions, a shear of 70 to 100, and a random flip, which resulted in a total of 84,204 CXR images. The increased data improved the performance of their proposed model.…”

Section: Augmentationmentioning

confidence: 99%

“…The model achieved state-of-the-art results using binary relevance classification for the 14 diseases of the used dataset, achieving an average AUC of 84.11%. Ait Nasser and Akhloufi [64] performed an ensemble learning of three different DCNN models (Xception, DenseNet-201, and EfficientNet-B5) to classify CXR images into three classes (normal, lung disease, and heart disease). A dataset of 26,316 CXR images was created by merging images from VinDr-CXR and CheXpert datasets.…”

Section: Multiple Disease Detectionmentioning

confidence: 99%

“…Therefore, preprocessing techniques were used by most of the studies, and they proved to be effective, especially when used with deep learning techniques that require the existence of a large amount of data. For data-augmentation, the recent papers employed traditional techniques [64], where others used GANs to generate artificial CXR images. This technique (GAN) is becoming widely used by researchers because of the results obtained, particularly in the medical field [157][158][159].…”

Section: Data Preprocessingmentioning

confidence: 99%

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A Review of Recent Advances in Deep Learning Models for Chest Disease Detection Using Radiography

Nasser

Akhloufi

2023

Diagnostics

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Chest X-ray radiography (CXR) is among the most frequently used medical imaging modalities. It has a preeminent value in the detection of multiple life-threatening diseases. Radiologists can visually inspect CXR images for the presence of diseases. Most thoracic diseases have very similar patterns, which makes diagnosis prone to human error and leads to misdiagnosis. Computer-aided detection (CAD) of lung diseases in CXR images is among the popular topics in medical imaging research. Machine learning (ML) and deep learning (DL) provided techniques to make this task more efficient and faster. Numerous experiments in the diagnosis of various diseases proved the potential of these techniques. In comparison to previous reviews our study describes in detail several publicly available CXR datasets for different diseases. It presents an overview of recent deep learning models using CXR images to detect chest diseases such as VGG, ResNet, DenseNet, Inception, EfficientNet, RetinaNet, and ensemble learning methods that combine multiple models. It summarizes the techniques used for CXR image preprocessing (enhancement, segmentation, bone suppression, and data-augmentation) to improve image quality and address data imbalance issues, as well as the use of DL models to speed-up the diagnosis process. This review also discusses the challenges present in the published literature and highlights the importance of interpretability and explainability to better understand the DL models’ detections. In addition, it outlines a direction for researchers to help develop more effective models for early and automatic detection of chest diseases.

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“…To classify CXR images into normal, lung disease, and heart illness categories, an ensemble learning strategy was proposed by combining photos from the VinDr-CXR and CheXpert databases. This strategy, along with data augmentation, achieved an average AUC of 94.89% [76]. A cascading neural network was used to classify the 14 illnesses from the ChestX-ray14 dataset.…”

Section: Multiple Disease Detectionmentioning

confidence: 99%

From Pixels to Pathology: Employing Computer Vision to Decode Chest Diseases in Medical Images

Arslan,

Haider,

Khurshid

et al. 2023

Cureus

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Radiology has been a pioneer in the healthcare industry's digital transformation, incorporating digital imaging systems like picture archiving and communication system (PACS) and teleradiology over the past thirty years. This shift has reshaped radiology services, positioning the field at a crucial junction for potential evolution into an integrated diagnostic service through artificial intelligence and machine learning. These technologies offer advanced tools for radiology's transformation. The radiology community has advanced computer-aided diagnosis (CAD) tools using machine learning techniques, notably deep learning convolutional neural networks (CNNs), for medical image pattern recognition. However, the integration of CAD tools into clinical practice has been hindered by challenges in workflow integration, unclear business models, and limited clinical benefits, despite development dating back to the 1990s. This comprehensive review focuses on detecting chest-related diseases through techniques like chest X-rays (CXRs), magnetic resonance imaging (MRI), nuclear medicine, and computed tomography (CT) scans. It examines the utilization of computer-aided programs by researchers for disease detection, addressing key areas: the role of computer-aided programs in disease detection advancement, recent developments in MRI, CXR, radioactive tracers, and CT scans for chest disease identification, research gaps for more effective development, and the incorporation of machine learning programs into diagnostic tools.

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Chest Diseases Classification Using CXR and Deep Ensemble Learning

Cited by 7 publications

References 15 publications

A Hybrid Deep Learning Architecture for Apple Foliar Disease Detection

A Hybrid Deep Learning Architecture for Apple Foliar Disease Detection

A Review of Recent Advances in Deep Learning Models for Chest Disease Detection Using Radiography

From Pixels to Pathology: Employing Computer Vision to Decode Chest Diseases in Medical Images

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