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

Kumar, B. Satheesh; Sathiyaprasad, B.

doi:10.3233/apc210064

Cited by 5 publications

(3 citation statements)

References 9 publications

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“…Kumar 17 developed a Decision tree and K‐nearest neighbor (KNN) approach for detecting bone cancer. Earlier detection of bone cancer with higher accuracy was one of the main advantages of this article.…”

Section: Related Workmentioning

confidence: 99%

An efficient convolutional histogram‐oriented gradients and deep convolutional learning approach for accurate classification of bone cancer

Vijayaraj,

Abirami,

Mohanty

et al. 2023

Int J Imaging Syst Tech

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In our human body bones are the most significant part, which helps people to move and perform several activities. But, the cancer is caused by producing abnormal cell, which is rapidly spread to the whole parts of the body. Bone cancer is one of the critical types due to its malignancy more than other cancers. The approach involves preprocessing and segmentation of input images to remove noise and resize images, followed by feature extraction using a Convolutional histogram of oriented gradients (ConvHisOrGrad). The ROI extraction helps to accurately identify abnormal parts around the cancerous area. The Extreme Deep Convolutional learning machine (Ex‐ConVLM) is used for normal and cancerous bone classification based on the texture properties of bone MRI images. The proposed technique was evaluated using a dataset of 220 bone MRIs for tumor classes classified as necrotic, non‐tumor, and viable‐tumor. Results showed that the proposed technique outperformed existing techniques with the highest accuracy of 97% for the necrotic tumor class, 98.2% for the non‐tumor class, and 98.6% for the viable tumor class. The fine‐tuned model shows promising performance in detecting malignancy in bone based on histological images. In summary, the proposed technique utilizes deep learning architectures and ROI extraction for the accurate identification of abnormal parts in bone MRI images, achieving state‐of‐the‐art performance in the detection and categorization of bone cancers.

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Section: Related Workmentioning

confidence: 99%

An efficient convolutional histogram‐oriented gradients and deep convolutional learning approach for accurate classification of bone cancer

Vijayaraj,

Abirami,

Mohanty

et al. 2023

Int J Imaging Syst Tech

View full text Add to dashboard Cite

show abstract

“…where 𝐾 ℎ = 𝐾/𝑛 acts as a scaling factor to keep the key's values, which use the estimated attention weights, at a constant value. For v values in sequence z, the output of the SA layers is determined as in (5):…”

Section: A Analyser Headmentioning

confidence: 99%

“…Mild tumours are generally not serious, apart from mild tumours that occur in the brain. Mild brain tumours can be dangerous and still lead to death [4], [5].…”

Section: Introductionmentioning

confidence: 99%

An Approach to Recognise Lung Diseases Using Segmentation and Classification

Prabakaran,

Selvaraj

2023

Measurement Science Review

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Lung cancer is one of the most common causes of death in people worldwide. One of the key procedures for early detection of cancer is segmentation or analysis and classification or assessment of lung images. Radiotherapists have to invest a lot of effort into the manual segmentation of medical images. To solve this issue, early-stage lung cancer is detected using Computed Tomography (CT) scan images. The proposed system for diagnosing lung cancer is divided into two main components: the first part is an analyser component built on the upper layer of the U-shaped Network Transformer (UNT), and the second component is an assessment component built on the upper layer of the self-supervised network, which is used to categorise the output segmentation component as benign or cancerous. The proposed method provides a powerful tool for the early detection and treatment of lung cancer by combining CT scan data with 2D input. Numerous experiments are conducted to improve the analysis and evaluation of the findings. Using the public dataset, both test and training experiments were conducted. New state-of-the-art performances were achieved with experimental results: an analyser accuracy of 96.9% and an assessment accuracy of 96.98%. The proposed approach provides a new powerful tool for leveraging 2D-input CT scan data for early detection and treatment of lung cancer using a variety of methods.

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Bone metastasis detection method based on improving golden jackal optimization using whale optimization algorithm

Magdy,

Abd Elaziz,

Elgarayhi

et al. 2023

Sci Rep

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This paper presents a machine learning-based technique for interpreting bone scintigraphy images, focusing on feature extraction and introducing a new feature selection method called GJOW. GJOW enhances the effectiveness of the golden jackal optimization (GJO) algorithm by integrating operators from the whale optimization algorithm (WOA). The technique’s performance is evaluated through extensive experiments using 18 benchmark datasets and 581 bone scan images obtained from a gamma camera, including 362 abnormal and 219 normal cases. The results highlight the superior predictive effectiveness of the GJOW algorithm in bone metastasis detection, achieving an accuracy of 71.79% and specificity of 91.14%. The contributions of this study include the introduction of a new machine learning-based approach for detecting bone metastasis using gamma camera scans, leading to improved accuracy in identifying bone metastases. The findings have practical implications for early detection and intervention, potentially improving patient outcomes.

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

Cited by 5 publications

References 9 publications

An efficient convolutional histogram‐oriented gradients and deep convolutional learning approach for accurate classification of bone cancer

An efficient convolutional histogram‐oriented gradients and deep convolutional learning approach for accurate classification of bone cancer

An Approach to Recognise Lung Diseases Using Segmentation and Classification

Bone metastasis detection method based on improving golden jackal optimization using whale optimization algorithm

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