Authors :
Elyse MUGABO; Dr. Wilson MUSONI
Volume/Issue :
Volume 8 - 2023, Issue 10 - October
Google Scholar :
https://tinyurl.com/5n8hxdm2
Scribd :
https://tinyurl.com/t8f94ssm
DOI :
https://doi.org/10.5281/zenodo.10040584
Abstract :
Deep leaning in orthopedic surgery has gained
mass interest over the last decade or so. In prior studies,
researchers have demonstrated that deep learning in
orthopedics can be used for different applications such as
fracture detection, bone tumor diagnosis, detecting hip
implant mechanical loosening, and grading osteoarthritis.
As time goes on, the utility of deep learning algorithms,
continues to grow and expand in orthopedic surgery. The
purpose of this research was to develop an extended
model of CNN algorithm in deep learning for bone tumor
detection and its application. Bone tumors can be
malignant growths. Despite the fact that it can happen in
any bone, it frequently happens in long bones like the
arms and legs. Although the exact source of this
malignant tumor is yet unknown, doctors believe that
DNA abnormalities within the bones are to blame. In
addition to destroying good bodily tissue, this results in
immature, crooked, and diseased bone. When a bone
tumor is suspected, the first test is a bone X-ray. The
greatest method for detecting cancer in the bones is
through imaging and X-ray scans. The recommended
procedure that can provide a certain diagnosis is a
biopsy. This labor-intensive and challenging process can
be mechanized. We presented a number of supervised
deep learning techniques and chose the suitable model.
To find bone cancer, a selection is made using the
weighted average of user data. Using the residual neural
network (ResNet101) technique, we extended the models
that were chosen and they met the expectations with the
maximum accuracy (90.36%) and precision (89.51%),
respectively, for the prediction tasks.
Keywords :
CNN: Convolutional Neural Networks, ANN: Artificial Neural Networks, MRI: Magnetic Resonance Imaging, AI: Artificial Intelligence.
Deep leaning in orthopedic surgery has gained
mass interest over the last decade or so. In prior studies,
researchers have demonstrated that deep learning in
orthopedics can be used for different applications such as
fracture detection, bone tumor diagnosis, detecting hip
implant mechanical loosening, and grading osteoarthritis.
As time goes on, the utility of deep learning algorithms,
continues to grow and expand in orthopedic surgery. The
purpose of this research was to develop an extended
model of CNN algorithm in deep learning for bone tumor
detection and its application. Bone tumors can be
malignant growths. Despite the fact that it can happen in
any bone, it frequently happens in long bones like the
arms and legs. Although the exact source of this
malignant tumor is yet unknown, doctors believe that
DNA abnormalities within the bones are to blame. In
addition to destroying good bodily tissue, this results in
immature, crooked, and diseased bone. When a bone
tumor is suspected, the first test is a bone X-ray. The
greatest method for detecting cancer in the bones is
through imaging and X-ray scans. The recommended
procedure that can provide a certain diagnosis is a
biopsy. This labor-intensive and challenging process can
be mechanized. We presented a number of supervised
deep learning techniques and chose the suitable model.
To find bone cancer, a selection is made using the
weighted average of user data. Using the residual neural
network (ResNet101) technique, we extended the models
that were chosen and they met the expectations with the
maximum accuracy (90.36%) and precision (89.51%),
respectively, for the prediction tasks.
Keywords :
CNN: Convolutional Neural Networks, ANN: Artificial Neural Networks, MRI: Magnetic Resonance Imaging, AI: Artificial Intelligence.
