Characterization of Trabecular Architecture in Human Femur Radiographic Images Using Directional Multiresolution Transform and AdaBoost Model

Bobby, T. Christy; Swaminathan, Ramakrishnan

doi:10.1007/978-3-642-35380-2_69

Cited by 1 publication

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“…The widely used feature selection algorithm for estimating the ranks and weights of the extracted features is Relieff algorithm [15]. Principal component analysis (PCA) is one of the significant dimensionality (features) reduction methods in data analysis and data mining which identifies patterns in data [16]. Support vector machine (SVM) is one of the marginal supervised machine learning methods which been used in heterogeneous medical imaging applications [17].…”

Section: Introductionmentioning

confidence: 99%

Benign and Malignant Dermatoscopy Image Classification

Bobby

2020

2020 Third International Conference on Advances in Electronics, Computers and Communications (ICAECC)

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Skin cancer is one of the life threatening diseases and there is mostly no chance of remission from skin cancer if diagnosed in the last stage. The three major types of skin cancers are basal cell carcinoma, squamous cell carcinoma, and melanoma. Among these three, melanoma skin cancer is dangerous and acute in nature. Dermatologists use various techniques for diagnosing the malignant, in which the popular and reliable clinical method is dermatoscopy. The manual inference of the disease condition from the dermatoscopy images requires intensive knowledge and experience in the related field. Also, there will be an unavoidable degree of variability in image analysis occurs as long as the diagnostics procedure relies on human visual perception. Thus recently, image processing and machine learning algorithms have been applied for the accurate diagnoses of skin cancers from the dermatoscopic images. Thus, the goal of the proposed work is to automatically segment and classify the dermatoscopy skin lesion image with the help of image processing and machine learning algorithms. The proposed approach classifies the skin lesion image as benign or malignant melanoma with 90% accuracy, 91% sensitivity, 86% specificity, and 93% precision.

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Section: Introductionmentioning

confidence: 99%

Benign and Malignant Dermatoscopy Image Classification

Bobby

2020

2020 Third International Conference on Advances in Electronics, Computers and Communications (ICAECC)

View full text Add to dashboard Cite

show abstract

Characterization of Trabecular Architecture in Human Femur Radiographic Images Using Directional Multiresolution Transform and AdaBoost Model

Cited by 1 publication

References 19 publications

Benign and Malignant Dermatoscopy Image Classification

Benign and Malignant Dermatoscopy Image Classification

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