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Chest x-ray images for lung disease detection using deep learning techniques: a comprehensive survey
MAA Al-qaness, J Zhu, D AL-Alimi, A Dahou… - … Methods in Engineering, 2024 - Springer
In medical imaging, the last decade has witnessed a remarkable increase in the availability
and diversity of chest X-ray (CXR) datasets. Concurrently, there has been a significant …
and diversity of chest X-ray (CXR) datasets. Concurrently, there has been a significant …
Analysis for diagnosis of pneumonia symptoms using chest X-ray based on MobileNetV2 models with image enhancement using white balance and contrast limited …
This study focuses on diagnosing pneumonia symptoms using chest X-ray (CXR) images. It
employs the MobileNetV2 model alongside image enhancement techniques, including white …
employs the MobileNetV2 model alongside image enhancement techniques, including white …
A robust hybrid deep convolutional neural network for covid-19 disease identification from chest x-ray images
The prompt and accurate identification of the causes of pneumonia is necessary to
implement rapid treatment and preventative approaches, reduce the burden of infections …
implement rapid treatment and preventative approaches, reduce the burden of infections …
Grid-search integrated optimized support vector machine model for breast cancer detection
Breast cancer is a common and highly heterogeneous cancer worldwide. Rapid detection
and early diagnosis are essential in its treatment, but it is challenging due to mammogram's …
and early diagnosis are essential in its treatment, but it is challenging due to mammogram's …
[HTML][HTML] Lightweight multi-scale classification of chest radiographs via size-specific batch normalization
Abstract Background and Objective: Convolutional neural networks are widely used to
detect radiological findings in chest radiographs. Standard architectures are optimized for …
detect radiological findings in chest radiographs. Standard architectures are optimized for …
A deep learning-based radiomics approach for covid-19 detection from cxr images using ensemble learning model
Medical image analysis plays a major role in aiding physicians in decision-making.
Specifically in detecting COVID-19, Deep Learning (DL) and radiomic approaches have …
Specifically in detecting COVID-19, Deep Learning (DL) and radiomic approaches have …
BrainSegNeT: a lightweight brain tumor segmentation model based on U-net and progressive neuron expansion
Brain tumor segmentation is a critical task in medical image analysis. In recent years,
several deep learning-based models have been developed for brain tumor segmentation …
several deep learning-based models have been developed for brain tumor segmentation …
A diagnosis model for brain atrophy using deep learning and MRI of type 2 diabetes mellitus
Objective Type 2 Diabetes Mellitus (T2DM) is linked to cognitive deterioration and
anatomical brain abnormalities like cerebral brain atrophy and cerebral diseases. We aim to …
anatomical brain abnormalities like cerebral brain atrophy and cerebral diseases. We aim to …
Comparison of mycobacterium tuberculosis image detection accuracy using CNN and combination CNN-KNN
Mycobacterium tuberculosis is a pathogenic bacterium that causes respiratory tract disease
in the lungs, namely tuberculosis (TB). The problem is to find out the bacterial colonies when …
in the lungs, namely tuberculosis (TB). The problem is to find out the bacterial colonies when …
Explainable AI assisted heart disease diagnosis through effective feature engineering and stacked ensemble learning
Heart disease presents significant challenges to healthcare systems globally, being the
primary cause of death and disability. Timely and accurate diagnosis is crucial for effective …
primary cause of death and disability. Timely and accurate diagnosis is crucial for effective …