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. 2024 Jan 25;14(1):2144.
doi: 10.1038/s41598-024-52719-8.

A comparative analysis of CNN-based deep learning architectures for early diagnosis of bone cancer using CT images

Kanimozhi Sampath  1 , Sivakumar Rajagopal  2 , Ananthakrishna Chintanpalli  3
Affiliations

A comparative analysis of CNN-based deep learning architectures for early diagnosis of bone cancer using CT images

Kanimozhi Sampath et al. Sci Rep. .

Abstract

Bone cancer is a rare in which cells in the bone grow out of control, resulting in destroying the normal bone tissue. A benign type of bone cancer is harmless and does not spread to other body parts, whereas a malignant type can spread to other body parts and might be harmful. According to Cancer Research UK (2021), the survival rate for patients with bone cancer is 40% and early detection can increase the chances of survival by providing treatment at the initial stages. Prior detection of these lumps or masses can reduce the risk of death and treat bone cancer early. The goal of this current study is to utilize image processing techniques and deep learning-based Convolution neural network (CNN) to classify normal and cancerous bone images. Medical image processing techniques, like pre-processing (e.g., median filter), K-means clustering segmentation, and, canny edge detection were used to detect the cancer region in Computer Tomography (CT) images for parosteal osteosarcoma, enchondroma and osteochondroma types of bone cancer. After segmentation, the normal and cancerous affected images were classified using various existing CNN-based models. The results revealed that AlexNet model showed a better performance with a training accuracy of 98%, validation accuracy of 98%, and testing accuracy of 100%.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Figure 1
Figure 1
Flowchart illustrating the steps involved in the detection of bone cancer.
Figure 2
Figure 2
The AlexNet architecture for detecting normal and cancerous CT bone images,.
Figure 3
Figure 3
Original CT images: (a) lateral CT of parosteal osteosarcoma, (b) coronal CT of Osteochondroma, and (c) lateral CT of Enchondroma.
Figure 4
Figure 4
Effect of the median filter: (a) lateral CT of parosteal osteosarcoma, (b) coronal CT of Osteochondroma, and (c) lateral CT of enchondroma.
Figure 5
Figure 5
Effect of K-means clustering: (a) lateral CT of Parosteal osteosarcoma, (b) coronal CT of osteochondroma, and (c) lateral CT of enchondroma.
Figure 6
Figure 6
Canny edge detection: (a) lateral CT of parosteal osteosarcoma, (b) coronal CT of osteochondroma, and (c) lateral CT of enchondroma.
Figure 7
Figure 7
Total weighted loss of AlexNet model during training and validation stages.
Figure 8
Figure 8
Accuracy of AlexNet model during training and validation stages.
See this image and copyright information in PMC

References

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