Authors :
Dr. B. Krishna; Teja Chalikanti; Bobbili Sreeja Reddy; Chanda Nithin Raj
Volume/Issue :
Volume 8 - 2023, Issue 8 - August
Google Scholar :
https://bit.ly/3TmGbDi
Scribd :
https://tinyurl.com/4kntab5w
DOI :
https://doi.org/10.5281/zenodo.8275894
Abstract :
The chest X-ray stands as a commonly
employed radiological examination for identifying
thoracic ailments. Despite the advancements brought by
convolutional neural network (CNN) techniques in
categorizing thoracic conditions through these images,
the varying scales of pathological irregularities across
different thoracic diseases present an ongoing challenge.
In response to these concerns, this study proposes a
refinement to the VGG19 model, a well-known residual
network architecture. This enhancement involves
integrating a pyramidal convolution module and a
shuffle attention module, both addressing the previously
mentioned issues. Specifically, the introduced VGG19
model leverages the shuffle attention mechanism to focus
on distinctive traits of pathological abnormalities. This
mechanism augments the capacity of the pyramid
convolution component, allowing it to extract more
discerning features related to pathological irregularities
compared to the conventional 3x3 convolution. Rigorous
assessments conducted on the ChestX-ray14 and
COVIDx datasets underscore the VGG19 model's
superior performance over other advanced
methodologies. Furthermore, an ablation study is
carried out to delve deeper into the impact of pyramidal
convolution and shuffle attention on enhancing the
classification efficacy of thoracic diseases. The study
results bolster the evidence indicating the effectiveness of
these integrated components in augmenting the model's
proficiency in thoracic disease classification.
Keywords :
VGG19, CXR (Chest X-ray), Consult Net, Pyramidal Convolution Module and Shuffle Attention Network (PCMSANet), Relu.
The chest X-ray stands as a commonly
employed radiological examination for identifying
thoracic ailments. Despite the advancements brought by
convolutional neural network (CNN) techniques in
categorizing thoracic conditions through these images,
the varying scales of pathological irregularities across
different thoracic diseases present an ongoing challenge.
In response to these concerns, this study proposes a
refinement to the VGG19 model, a well-known residual
network architecture. This enhancement involves
integrating a pyramidal convolution module and a
shuffle attention module, both addressing the previously
mentioned issues. Specifically, the introduced VGG19
model leverages the shuffle attention mechanism to focus
on distinctive traits of pathological abnormalities. This
mechanism augments the capacity of the pyramid
convolution component, allowing it to extract more
discerning features related to pathological irregularities
compared to the conventional 3x3 convolution. Rigorous
assessments conducted on the ChestX-ray14 and
COVIDx datasets underscore the VGG19 model's
superior performance over other advanced
methodologies. Furthermore, an ablation study is
carried out to delve deeper into the impact of pyramidal
convolution and shuffle attention on enhancing the
classification efficacy of thoracic diseases. The study
results bolster the evidence indicating the effectiveness of
these integrated components in augmenting the model's
proficiency in thoracic disease classification.
Keywords :
VGG19, CXR (Chest X-ray), Consult Net, Pyramidal Convolution Module and Shuffle Attention Network (PCMSANet), Relu.
