Develop an Extended Model of CNN Algorithm in Deep Learning for Bone Tumor Detection and its Application


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.

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