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Computer Aided Theragnosis Using Quantitative Ultrasound Spectroscopy and Maximum Mean Discrepancy in Locally Advanced Breast Cancer

Computer Aided Theragnosis Using Quantitative Ultrasound Spectroscopy and Maximum Mean Discrepancy in Locally Advanced Breast Cancer Computer Aided Theragnosis Using Quantitative Ultrasound Spectroscopy and Maximum Mean Discrepancy in Locally Advanced Breast Cancer Abstract: A noninvasive computer-aided-theragnosis (CAT) system was developed for the early therapeutic cancer response assessment in patients with locally advanced breast cancer (LABC) treated with neoadjuvant chemotherapy. The proposed CAT system was based on multi-parametric quantitative ultrasound (QUS) spectroscopic methods in conjunction with advanced machine learning techniques. Specifically, a kernel-based metric…

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Machine Learning Methods for Binary and Multiclass Classification of Melanoma Thickness From Dermoscopic Images

Machine Learning Methods for Binary and Multiclass Classification of Melanoma Thickness From Dermoscopic Images Machine Learning Methods for Binary and Multiclass Classification of Melanoma Thickness From Dermoscopic Images Abstract: Thickness of the melanoma is the most important factor associated with survival in patients with melanoma. It is most commonly reported as a measurement of depth given in millimeters (mm) and computed by means of pathological examination after a biopsy of the suspected lesion. In order to avoid the use of an invasive method…

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Melanoma Is Skin Deep: A 3D Reconstruction Technique for Computerized Dermoscopic Skin Lesion Classification

Melanoma Is Skin Deep: A 3D Reconstruction Technique for Computerized Dermoscopic Skin Lesion Classification Melanoma Is Skin Deep: A 3D Reconstruction Technique for Computerized Dermoscopic Skin Lesion Classification Abstract: Melanoma mortality rates are the highest amongst skin cancer patients. Melanoma is life threating when it grows beyond the dermis of the skin. Hence, depth is an important factor to diagnose melanoma. This paper introduces a non-invasive computerized dermoscopy system that considers the estimated depth of skin lesions for diagnosis. A 3-D skin lesion…

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Brain tumour classification using two-tier classifier with adaptive segmentation technique

Brain tumour classification using two-tier classifier with adaptive segmentation technique Brain tumour classification using two-tier classifier with adaptive segmentation technique Abstract: A brain tumour is a mass of tissue that is structured by a gradual addition of anomalous cells and it is important to classify brain tumours from the magnetic resonance imaging (MRI) for treatment. Human investigation is the routine technique for brain MRI tumour detection and tumours classification. Interpretation of images is based on organised and explicit classification of brain MRI and…

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Gene selection approach based on improved swarm intelligent optimisation algorithm for tumour classification

Gene selection approach based on improved swarm intelligent optimisation algorithm for tumour classification Gene selection approach based on improved swarm intelligent optimisation algorithm for tumour classification Abstract: A number of different gene selection approaches based on gene expression profiles (GEP) have been developed for tumour classification. A gene selection approach selects the most informative genes from the whole gene space, which is an important process for tumour classification using GEP. This study presents an improved swarm intelligent optimisation algorithm to select genes for…

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Laryngeal Tumor Detection and Classification in Endoscopic Video

Laryngeal Tumor Detection and Classification in Endoscopic Video Laryngeal Tumor Detection and Classification in Endoscopic Video Abstract: The development of the narrow-band imaging (NBI) has been increasing the interest of medical specialists in the study of laryngeal microvascular network to establish diagnosis without biopsy and pathological examination. A possible solution to this challenging problem is presented in this paper, which proposes an automatic method based on anisotropic filtering and matched filter to extract the lesion area and segment blood vessels. Lesion classification is…

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A Hybrid Feature Selection With Ensemble Classification for Imbalanced Healthcare Data: A Case Study for Brain Tumor Diagnosis

A Hybrid Feature Selection With Ensemble Classification for Imbalanced Healthcare Data: A Case Study for Brain Tumor Diagnosis A Hybrid Feature Selection With Ensemble Classification for Imbalanced Healthcare Data: A Case Study for Brain Tumor Diagnosis Abstract: Electronic health records (EHRs) are providing increased access to healthcare data that can be made available for advanced data analysis. This can be used by the healthcare professionals to make a more informed decision providing improved quality of care. However, due to the inherent heterogeneous and…

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