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Chest X-rays remains to be the most common imaging modality used to diagnose lung diseases. However, they necessitate the interpretation of experts (radiologists and pulmonologists), who are few. This review paper investigates the use of…

图像与视频处理 · 电气工程与系统科学 2023-03-30 Irad Mwendo , Kinyua Gikunda , Anthony Maina

In this paper, we have trained several deep convolutional networks with introduced training techniques for classifying X-ray images into three classes: normal, pneumonia, and COVID-19, based on two open-source datasets. Our data contains…

图像与视频处理 · 电气工程与系统科学 2021-05-05 Mohammad Rahimzadeh , Abolfazl Attar

Deep learning integration into medical imaging systems has transformed disease detection and diagnosis processes with a focus on pneumonia identification. The study introduces an intricate deep learning system using Convolutional Neural…

图像与视频处理 · 电气工程与系统科学 2025-10-02 P K Dutta , Anushri Chowdhury , Anouska Bhattacharyya , Shakya Chakraborty , Sujatra Dey

According to the World Health Organization (WHO), pneumonia is a disease that causes a significant number of deaths each year. In response to this issue, the development of a decision support system for the classification of patients into…

图像与视频处理 · 电气工程与系统科学 2025-03-06 Carlos Arizmendi , Jorge Pinto , Alejandro Arboleda , Hernando González

Pneumonia, particularly when induced by diseases like COVID-19, remains a critical global health challenge requiring rapid and accurate diagnosis. This study presents a comprehensive comparison of traditional machine learning and…

图像与视频处理 · 电气工程与系统科学 2025-07-16 Gaurav Singh

The global pandemic of COVID-19 is continuing to have a significant effect on the well-being of global population, increasing the demand for rapid testing, diagnosis, and treatment. Along with COVID-19, other etiologies of pneumonia and…

Coronavirus disease (COVID-19) is a pandemic disease, which has already caused thousands of causalities and infected several millions of people worldwide. Any technological tool enabling rapid screening of the COVID-19 infection with high…

Diagnosing lung inflammation, particularly pneumonia, is of paramount importance for effectively treating and managing the disease. Pneumonia is a common respiratory infection caused by bacteria, viruses, or fungi and can indiscriminately…

神经与进化计算 · 计算机科学 2023-10-06 Mehdi Neshat , Muktar Ahmed , Hossein Askari , Menasha Thilakaratne , Seyedali Mirjalili

Chest X-ray imaging is commonly used to diagnose pneumonia, but accurately localizing the pneumonia-affected regions typically requires detailed pixel-level annotations, which are costly and time consuming to obtain. To address this…

计算机视觉与模式识别 · 计算机科学 2026-01-15 Kiran Shahi , Anup Bagale

The possibility of high-precision and rapid detection of pathologies on chest X-rays makes it possible to detect the development of pneumonia at an early stage and begin immediate treatment. Artificial intelligence can speed up and…

图像与视频处理 · 电气工程与系统科学 2023-03-21 Kolesnikov Dmitry

To develop a deep-learning model that integrates radiomics analysis for enhanced performance of COVID-19 and Non-COVID-19 pneumonia detection using chest X-ray image, two deep-learning models were trained based on a pre-trained VGG-16…

图像与视频处理 · 电气工程与系统科学 2022-10-12 Zongsheng Hu , Zhenyu Yang , Kyle J. Lafata , Fang-Fang Yin , Chunhao Wang

Respiratory diseases kill million of people each year. Diagnosis of these pathologies is a manual, time-consuming process that has inter and intra-observer variability, delaying diagnosis and treatment. The recent COVID-19 pandemic has…

图像与视频处理 · 电气工程与系统科学 2020-12-01 Juan E. Arco , A. Ortiz , J. Ramirez , F. J. Martinez-Murcia , Yu-Dong Zhang , Juan M. Gorriz

Identifying who is infected with the Covid-19 virus is critical for controlling its spread. X-ray machines are widely available worldwide and can quickly provide images that can be used for diagnosis. A number of recent studies claim it may…

图像与视频处理 · 电气工程与系统科学 2021-02-09 Kaoutar Ben Ahmed , Lawrence O. Hall , Dmitry B. Goldgof , Gregory M. Goldgof , Rahul Paul

Pneumonia is a leading cause of mortality in children under five, with over 700,000 deaths annually. Accurate diagnosis from chest X-rays is limited by radiologist availability and variability. Objective: This study compares custom CNNs…

计算机视觉与模式识别 · 计算机科学 2026-01-06 Agniv Roy Choudhury

Pneumonia has been one of the fatal diseases and has the potential to result in severe consequences within a short period of time, due to the flow of fluid in lungs, which leads to drowning. If not acted upon by drugs at the right time,…

计算机视觉与模式识别 · 计算机科学 2019-04-02 Can Jozef Saul , Deniz Yagmur Urey , Can Doruk Taktakoglu

Background: Novel coronavirus disease has spread rapidly worldwide. As recent radiological literatures on Covid-19 related pneumonia is primarily focused on CT findings, the American College of Radiology (ACR) recommends using portable…

Pneumonia, a respiratory infection brought on by bacteria or viruses, affects a large number of people, especially in developing and impoverished countries where high levels of pollution, unclean living conditions, and overcrowding are…

图像与视频处理 · 电气工程与系统科学 2024-09-02 Al Mohidur Rahman Porag , Md. Mahedi Hasan , Md Taimur Ahad

While deep learning has shown promise in the domain of disease classification from medical images, models based on state-of-the-art convolutional neural network architectures often exhibit performance loss due to dataset shift. Models…

机器学习 · 计算机科学 2020-01-14 Joseph D. Janizek , Gabriel Erion , Alex J. DeGrave , Su-In Lee

We found that the real time reverse transcription-polymerase chain reaction (RT-PCR) detection of viral RNA from sputum or nasopharyngeal swab has a relatively low positive rate in the early stage to determine COVID-19 (named by the World…

Manual identification and classification of pneumonia and COVID-19 infection is a cumbersome process that, if delayed can cause irreversible damage to the patient. We have compiled CT scan images from various sources, namely, from the China…

图像与视频处理 · 电气工程与系统科学 2024-04-22 Amit Karanth Gurpur , Janani S , Ajeetha B , Brintha Therese A , Rajeswaran Rangasami