Authors :
PAVITHRA S; SUPRAJA A; SHANMUGAPRIYA G; S. SARANYA
Volume/Issue :
Volume 7 - 2022, Issue 6 - June
Google Scholar :
https://bit.ly/3IIfn9N
Scribd :
https://bit.ly/3ujIOdJ
DOI :
https://doi.org/10.5281/zenodo.6789421
Abstract :
Renal calculi, often known as kidney stones,
are solid masses made mostly of crystals. Detecting the
perfect and correct site of urinary calculus is essential for
surgical procedures. Because CT pictures include
greater speckle noise, manually detecting urinary calculi
is difficult, hence automated systems for detecting kidney
stones in CT images are necessary. CT imaging is one of
the imaging modalities available for diagnosing kidney
abnormalities, which can include changes in shape and
position, as well as swelling of the limb; other kidney
abnormalities include the creation of stones, cysts, urine
obstruction, congenital defects, and malignant cells. This
research presents a new method for detecting kidney
stones. This project is divided into two parts: kidney CT
image classification and kidney stone detection. For renal
CT image classification, VGG16 convolutional neural
networks are employed, and for kidney stone
identification, Fuzzy c means clustering is used. Filtering
and processing of kidney CT scans removes undesired
noise and enhances the image. Using complicated
techniques such as the Convolutional Neural Network
(CNN) model, classify the image using the SoftMax
classifier algorithm, and ultimately detect the kidney
stone using FCM. This technology will produce more
accurate findings and will do it faster than the previous
way.
Keywords :
Kidney Stones, Image Processing, CT image, CNN.
Renal calculi, often known as kidney stones,
are solid masses made mostly of crystals. Detecting the
perfect and correct site of urinary calculus is essential for
surgical procedures. Because CT pictures include
greater speckle noise, manually detecting urinary calculi
is difficult, hence automated systems for detecting kidney
stones in CT images are necessary. CT imaging is one of
the imaging modalities available for diagnosing kidney
abnormalities, which can include changes in shape and
position, as well as swelling of the limb; other kidney
abnormalities include the creation of stones, cysts, urine
obstruction, congenital defects, and malignant cells. This
research presents a new method for detecting kidney
stones. This project is divided into two parts: kidney CT
image classification and kidney stone detection. For renal
CT image classification, VGG16 convolutional neural
networks are employed, and for kidney stone
identification, Fuzzy c means clustering is used. Filtering
and processing of kidney CT scans removes undesired
noise and enhances the image. Using complicated
techniques such as the Convolutional Neural Network
(CNN) model, classify the image using the SoftMax
classifier algorithm, and ultimately detect the kidney
stone using FCM. This technology will produce more
accurate findings and will do it faster than the previous
way.
Keywords :
Kidney Stones, Image Processing, CT image, CNN.
