SVM Model based Computerized Bone Cancer Detection

Jabber, Bhukya; Shankar, M. Ravi; Rao, P. Venkateswara; Krishna, Azmira; Basha, Cmak Zeelan

doi:10.1109/iceca49313.2020.9297624

Cited by 20 publications

(3 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This approach improved the peak signal-to-noise ratio (PSNR) in denoising medical images corrupted by medium to high noise densities by 1.2 dB. The authors of [16] propose a method for bone cancer detection using simple statistical feature extraction and SVM-based computerized classification. They used the maximum value among the mean and median values to replace each pixel for filtering.…”

Section: Methodsmentioning

confidence: 99%

DTBV: A Deep Transfer-Based Bone Cancer Diagnosis System Using VGG16 Feature Extraction

et al. 2023

View full text Add to dashboard Cite

Among the many different types of cancer, bone cancer is the most lethal and least prevalent. More cases are reported each year. Early diagnosis of bone cancer is crucial since it helps limit the spread of malignant cells and reduce mortality. The manual method of detection of bone cancer is cumbersome and requires specialized knowledge. A deep transfer-based bone cancer diagnosis (DTBV) system using VGG16 feature extraction is proposed to address these issues. The proposed DTBV system uses a transfer learning (TL) approach in which a pre-trained convolutional neural network (CNN) model is used to extract features from the pre-processed input image and a support vector machine (SVM) model is used to train using these features to distinguish between cancerous and healthy bone. The CNN is applied to the image datasets as it provides better image recognition with high accuracy when the layers in neural network feature extraction increase. In the proposed DTBV system, the VGG16 model extracts the features from the input X-ray image. A mutual information statistic that measures the dependency between the different features is then used to select the best features. This is the first time this method has been used for detecting bone cancer. Once selected features are selected, they are fed into the SVM classifier. The SVM model classifies the given testing dataset into malignant and benign categories. A comprehensive performance evaluation has demonstrated that the proposed DTBV system is highly efficient in detecting bone cancer, with an accuracy of 93.9%, which is more accurate than other existing systems.

show abstract

Section: Methodsmentioning

confidence: 99%

DTBV: A Deep Transfer-Based Bone Cancer Diagnosis System Using VGG16 Feature Extraction

et al. 2023

View full text Add to dashboard Cite

show abstract

“…Secondly, a spatial attention feature aggregation operator was suggested for improvising spatial location data. In [16], an automated bone tumor recognition system has been suggested to help oncologists in the initial identification of bone tumours and aids them in doing prompt medication. SVM-related Fuzzy C-Means (FCM) and M3-filtered segmentation approaches were recommended for deducting the bone tumors.…”

Section: Related Workmentioning

confidence: 99%

Bone Cancer Detection and Classification Using Owl Search Algorithm With Deep Learning on X-Ray Images

Alabdulkreem,

Saeed,

Alotaibi

et al. 2023

IEEE Access

View full text Add to dashboard Cite

Bone cancer is treated as a severe health problem, and, in many cases, it causes patient death. Early detection of bone cancer is efficient in reducing the spread of malignant cells and decreasing mortality. Since the manual detection process is a laborious task, it is needed to design an automated system to classify and identify the cancerous bone and the healthy bone. Therefore, this article develops an Owl Search Algorithm with a Deep Learning-Driven Bone Cancer Detection and Classification (OSADL-BCDC) technique. The OSADL-BCDC algorithm follows the principle of transfer learning with a hyperparameter tuning strategy for bone cancer detection. The OSADL-BCDC model employs Inception v3 as a pretrained model for the feature extraction process which does not necessitate a manual segmentation of X-ray images. Besides, the OSA is applied as a hyperparameter optimizer for enhancing the efficacy of the Inception v3 method. Finally, the long short-term memory (LSTM) approach is used for identifying the presence of bone cancer. The proposed OSADL-BCDC technique reduces diagnosis time and achieves faster convergence. The experimental analysis of the OSADL-BCDC algorithm is tested using a set of medical images and the outcomes were measured under different aspects. The comparison study highlighted the improved performance of the OSADL-BCDC model over existing algorithms.

show abstract

“…The tumor area is isolated by applying a clustering computation exclusively to the border pixels identified by the Sobel edge locator. Jabber et al 26 have also developed similar methods for detecting bone cancer in MRI images using medical image processing techniques. Their suggested preprocessing procedures involve noise removal and clutter reduction using the Gabor filter.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Bone Cancer Diagnosis Through Image Extraction and Machine Learning: A State-of-the-Art Approach

Shrivastava,

Nag

2023

Surg Innov

View full text Add to dashboard Cite

Background: Bone cancer is a severe condition often leading to patient mortality. Diagnosis relies on X-rays, MRIs, or CT scans, which require time-consuming manual review by experts. Thus, developing an automated system is crucial for accurate classification of malignant and healthy bone. Methods: Differentiating between them poses a challenge as they may exhibit similar physical characteristics. The initial step is selecting the optimal edge detection method. Two feature sets are then generated: one with the histogram of oriented gradients (HOG) and one without. Performance evaluation involves two machine learning models: Support Vector Machine (SVM) and Random Forest. Results: Including HOG consistently yields superior results. The SVM model with HOG achieves an F-1 score of 0.92, outperforming the Random Forest model’s .77. This study aims to develop reliable methods for bone cancer classification. The proposed automated method assists surgeons in accurately detecting malignant bone regions using modern image analysis techniques and machine learning models. Incorporating HOG significantly enhances performance, improving differentiation between malignant and healthy bone. Conclusion: Ultimately, this approach supports precise diagnoses and informed treatment decisions for bone cancer patients.

show abstract

SVM Model based Computerized Bone Cancer Detection

Cited by 20 publications

References 14 publications

DTBV: A Deep Transfer-Based Bone Cancer Diagnosis System Using VGG16 Feature Extraction

DTBV: A Deep Transfer-Based Bone Cancer Diagnosis System Using VGG16 Feature Extraction

Bone Cancer Detection and Classification Using Owl Search Algorithm With Deep Learning on X-Ray Images

Enhancing Bone Cancer Diagnosis Through Image Extraction and Machine Learning: A State-of-the-Art Approach

Contact Info

Product

Resources

About