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

Sep 21, 2026

Uploaded by Logic

A comparative study evaluates convolutional neural network (CNN) architectures for early bone-cancer diagnosis using computed tomography images. The workflow applies medical image processing steps to locate cancer regions, including preprocessing with filters, K-means clustering or other segmentation approaches, and edge-based detection. After segmentation, multiple existing CNN models classify normal versus cancerous bone images. Reported performance highlights AlexNet with high training and validation accuracy and perfect testing accuracy, alongside discussion of related classical feature-based pipelines and comparative results.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages9

Reading Time23 min

AccessFree to download

Study objective and dataset context

Preprocessing and segmentation workflow

CNN-based classification and model comparison

[www. nature.com/scientificreports](www. nature.com/scientificreports) OPEN A comparative analysis of CNN‑based deep learning architectures for early diagnosis of bone cancer using CT images Kanimozhi Sampath1, Sivakumar Rajagopal1* & Ananthakrishna Chintanpalli2 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% . Bones are made of two regions, outer and inner regions. The outer region is compact and enclosed by cancellous tissues while the inner region consists of blood-producing material1. Bone cancer can originate from any part of the bones and can occur due to hereditary factors or previous radiation exposure. The benign cancer occurs commonly and is asymptomatic until the disease spreads or injuries the other body parts. The malignant cancer can lead to the patient’s death unless treated at the early stage2. Since most of the cancers are asymptomatic, early diagnosis and treatment is critical to stop spreading to the other regions of the body. Bone cancer is divided into primary and secondary types. If the unrestricted cell growth is not treated during the primary type, cancer can develop unwanted new cells which may later lead to death. In the primary type, cancer starts from cells of bone whereas in the secondary type, cancer starts from other body regions and then affect the cells of the bone3. Primary detection of bone cancer has a chance of reducing the death rate. In the beginning stage, the symptoms of bone cancer may include bowel movement change, formation of new lumps, weight loss, bone loss, pain and, weakness in bones4. Proper treatment of cancer requires information like the history of patients, physical examination, and imaging techniques (e.g., X-ray2, Computed Tomography (CT)5, Magnetic Resonance Imaging (MRI)6, and Positron Emission Tomography (PET)7). Radiologists prefer medical imaging procedure for the detection of cancer due to the management of time, low cost and early detection. The preprocessing, segmentation, feature extraction, and classification stages are incorporated in medical devices for early diagnosis8. Moreover, the pre-processing stage includes, either bilateral, median or Gaussian filter to remove the noise from the images9, 10. After the noise removal, cancer regions can be segmented either using the threshold based11, region based11, 12 or edge based segmentation13 methods. The segmentation techniques like Prewitt, Canny, Sobel, K-means and region growing were used to analyze the osteosarcoma type of bone cancer in X-ray images2, 10, 13. The K-means and edge detection segmentation algorithms have also been used for bone cancer14. After segmenting the cancer regions, seven Gray Level Co-occurrence Matrix (GLCM) features were extr

FAQs

What is the main goal of the study?

To use CNN-based deep learning and image processing to classify normal and cancerous bone images for early diagnosis.

Which image processing steps are used to detect cancer regions?

The pipeline includes preprocessing (e.g., median filtering), segmentation such as K-means clustering, and edge detection methods (e.g., Canny) on CT images.

How did AlexNet perform in the reported results?

AlexNet showed strong performance, with training accuracy around 98%, validation accuracy around 98%, and testing accuracy reported as 100%.

Sep 21, 2026

Uploaded by Logic