Why the study?
Accurate interpretation of chest radiographs requires significant experience and time, potentially leading to diagnostic and treatment delays.
Does a deep learning-based method using CNNs accurately identify chest disorders in radiographs?
Does a deep learning-based method using CNNs accurately identify chest disorders in radiographs?
A deep learning-based method using convolutional neural networks is proposed to automate the detection of pulmonary diseases on chest radiographs.
Proposed CNN method for pulmonary disease detection requires validation; leaves open clinical utility and accuracy in practice.
One of the most widely used and easily accessible diagnostic methods for identifying a variety of pulmonary disorders is still chest radiography. However, interpreting chest radiographs accurately requires significant experience and time, which can lead to delays in diagnosis and treatment. To enhance the precision and efficiency of diagnosis, we present a deep learning-based method for the automated identification of chest disorders in radiographs. Our approach analyzes chest radiographs and detects anomalies indicative of various pulmonary diseases using convolutional neural networks (CNNs), a type of deep learning model well-suited for image recognition tasks. We utilize a large collection of annotated chest radiographs covering a wide range of pathological conditions, including pulmonary edema, lung cancer, pneumonia, and tuberculosis.
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E Pranesh (2024) studied this question.
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