Integrated approach for bone tumor detection from MRI scan imagery

Mistry, Krupali D.; Talati, Bijal

doi:10.1109/iconsip.2016.7857471

Cited by 14 publications

(2 citation statements)

References 7 publications

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“…KrupaliDMistry et al explained the method in identifying the bone cancer by combining the feature extraction using clustering algorithm for input MRI images. The tumor cells has been identified after segmentation through mean pixel intensity where it is used in detecting enchondroma bone cancer [7]. MadhuriAvula et.al.…”

Section: Related Workmentioning

confidence: 99%

Bone Cancer Detection Using Feature Extraction with Classification Using K-Nearest Neighbor and Decision Tree Algorithm

Kumar

Sathiyaprasad

2021

Advances in Parallel Computing

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The malignant cells that cannot be controlled from spreading throughout the body is Cancer. Among which the cancer occurs in bone is their type. It is malignant disease occur in bone of human body where their growth cant be controlled from growing. This bone cancer is very critical of all the cancer types since the malignant cells are not identified at their earlier stage and it is the major challenge. Bone cancer is highly common for children and teenagers. For earlier detection of this cancer the correlation of medical imaging has been adapted with image processing and machine learning techniques where maximum accuracy can be obtained similarly even for bone cancer. This paper proposes the detection of bone cancer from the dataset taken from clinical dataset. Here the proposed design comprises of 2 phases in predicting the disorder with higher accuracy. The first stage is extracting the feature of segmented bone image using Gray-Level Co-occurrence Matrix (GLCM) method is applied to extract the features in terms of statistical texture-based and the second phase is classification of extracted feature using K-NN with decision tree algorithm. The simulation results show the enhanced classification results and extracted output with higher accuracy.

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Section: Related Workmentioning

confidence: 99%

Bone Cancer Detection Using Feature Extraction with Classification Using K-Nearest Neighbor and Decision Tree Algorithm

Kumar

Sathiyaprasad

2021

Advances in Parallel Computing

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“…Solid phase microextraction is an advanced, solvent-free sample prep technology that is fast, cheap, and multipurpose. [9]. In this day and age, CT scans of the lung area are known to be constructive in the diagnosis of lung cancer.…”

Section: Introductionmentioning

confidence: 99%

Identification and Classification of Lung Nodules Using Neural Networks

Gnanavel¹

2021

TURCOMAT

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Lung cancer is a serious health concern, which is also one of the major types of cancer that has a profound impact on the overall cancer mortality rates. The detection of lung cancer nodules is quite a challenge as the major challenge is the structure of the cancer nodules; here the cells are imbricated with each other. The prediction and classification of lung cancer is done by applying digital image processing techniques to the acquired input images of the nodules. This methodology also aids early detection which in turns reduces the criticality of the condition and provides scope for early intervention and treatment. The prediction methodology involves extracting several features of the lung cancer cell and then applying pattern-based prediction techniques. In recent times, owing to the fact that the time and execution parameters are very important aspects to detect the abnormality of the fast-spreading cancer cells, digital image processing techniques are being widely deployed. The fundamental factors of this research are the quality of image assessment and the precision of feature extraction. Following our proposed methodology, a clear picture of the region of interest is obtained which acts as a basis for the feature extraction process. Here an overall evaluation of the digital image processing techniques used by previous scholars for the finding and classification of lung cancer nodules have also been emphasised.

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