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 named maximum mean discrepancy (MMD), a technique for learning from imbalanced data based on random undersampling, and supervised learning were investigated with response-monitoring data from LABC patients. The CAT system was tested on 56 patients using statistical significance tests and leave-one-subject-out classification techniques. Textural features using state-of-the-art local binary patterns (LBP), and gray-scale intensity features were extracted from the spectral parametric maps in the proposed CAT system. The system indicated significant differences in changes between the responding and non-responding patient populations as well as high accuracy, sensitivity, and specificity in discriminating between the two patient groups early after the start of treatment, i.e., on weeks 1 and 4 of several months of treatment. The proposed CAT system achieved an accuracy of 85%, 87%, and 90% on weeks 1, 4 and 8, respectively. The sensitivity and specificity of developed CAT system for the same times was 85%, 95%, 90% and 85%, 85%, 91%, respectively. The proposed CAT system thus establishes a noninvasive framework for monitoring cancer treatment response in tumors using clinical ultrasound imaging in conjunction with machine learning techniques. Such a framework can potentially facilitate the detection of refractory responses in patients to treatment early on during a course of therapy to enable possibly switching to more efficacious treatments.
Published in: IEEE Transactions on Medical Imaging ( Volume: 35, Issue: 3, March 2016 )
Date of Publication: 27 October 2015
ISSN Information:
PubMed ID: 26529750
INSPEC Accession Number: 15823116
Publisher: IEEE

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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 in the estimation of the thickness of melanoma before surgery, we propose a computational image analysis system from dermoscopic images. The proposed feature extraction is based on the clinical findings that correlate certain characteristics present in dermoscopic images and tumor depth. Two supervised classification schemes are proposed: a binary classification in which melanomas are classified into thin or thick, and a three-class scheme (thin, intermediate, and thick). The performance of several nominal classification methods, including a recent interpretable method combining logistic regression with artificial neural networks (Logistic regression using Initial variables and Product Units, LIPU), is compared. For the three-class problem, a set of ordinal classification methods (considering ordering relation between the three classes) is included. For the binary case, LIPU outperforms all the other methods with an accuracy of 77.6%, while, for the second scheme, although LIPU reports the highest overall accuracy, the ordinal classification methods achieve a better balance between the performances of all classes.
Published in: IEEE Transactions on Medical Imaging ( Volume: 35, Issue: 4, April 2016 )
ate of Publication: 07 December 2015
ISSN Information:
PubMed ID: 26672031
INSPEC Accession Number: 15902266
Publisher: IEEE

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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 reconstruction technique using the estimated depth obtained from regular dermoscopic images is presented. On basis of the 3-D reconstruction, depth and 3-D shape features are extracted. In addition to 3-D features, regular color, texture, and 2-D shape features are also extracted. Feature extraction is critical to achieve accurate results. Apart from melanoma, in-situ melanoma the proposed system is designed to diagnose basal cell carcinoma, blue nevus, dermatofibroma, haemangioma, seborrhoeic keratosis, and normal mole lesions. For experimental evaluations, the PH2, ISIC: Melanoma Project, and ATLAS dermoscopy data sets is considered. Different feature set combinations is considered and performance is evaluated. Significant performance improvement is reported the post inclusion of estimated depth and 3-D features. The good classification scores of sensitivity = 96%, specificity = 97% on PH2 data set and sensitivity = 98%, specificity = 99% on the ATLAS data set is achieved. Experiments conducted to estimate tumor depth from 3-D lesion reconstruction is presented. Experimental results achieved prove that the proposed computerized dermoscopy system is efficient and can be used to diagnose varied skin lesion dermoscopy images.
Article Sequence Number: 4300117
Date of Publication: 16 January 2017
Online ISSN: 2168-2372
INSPEC Accession Number: 16635744
Publisher: IEEE

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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 also various techniques have been proposed. Information identified with anatomical structures and potential abnormal tissues which are noteworthy to treat are given by brain tumour segmentation on MRI, the proposed system uses the adaptive pillar K-means algorithm for successful segmentation and the classification methodology is done by the two-tier classification approach. In the proposed system, at first the self-organising map neural network trains the features extracted from the discrete wavelet transform blend wavelets and the resultant filter factors are consequently trained by the K-nearest neighbour and the testing process is also accomplished in two stages. The proposed two-tier classification system classifies the brain tumours in double training process which gives preferable performance over the traditional classification method. The proposed system has been validated with the support of real data sets and the experimental results showed enhanced performance.
Published in: IET Computer Vision ( Volume: 10, Issue: 1, 2 2016 )
Date of Publication: 18 January 2016
ISSN Information:
INSPEC Accession Number: 15688954
Publisher: IET

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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 maintaining the diversity of the population. The most essential characteristic of the proposed approach is that it can automatically determine the number of the selected genes. On the basis of the gene selection, the authors construct a variety of the tumour classifiers, including the ensemble classifiers. Four gene datasets are used to evaluate the performance of the proposed approach. The experimental results confirm that the proposed classifiers for tumour classification are indeed effective.
Published in: IET Systems Biology ( Volume: 10, Issue: 3, 6 2016 )
Date of Publication: 19 May 2016
ISSN Information:
INSPEC Accession Number: 15987211
Publisher: IET

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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 then performed based on a statistical analysis of the blood vessels’ characteristics, such as thickness, tortuosity, and density. Here, the presented algorithm is applied to 50 NBI endoscopic images of laryngeal diseases and the segmentation and classification accuracies are investigated. The experimental results show the proposed algorithm provides reliable results, reaching an overall classification accuracy rating of 84.3%. This is a highly motivating preliminary result that proves the feasibility of the new method and supports the investment in further research and development to translate this study into clinical practice. Furthermore, to our best knowledge, this is the first time image processing is used to automatically classify laryngeal tumors in endoscopic videos based on tumor vascularization characteristics. Therefore, the introduced system represents an innovation in biomedical and health informatics.
Published in: IEEE Journal of Biomedical and Health Informatics ( Volume: 20, Issue: 1, Jan. 2016 )
Date of Publication: 25 November 2014
ISSN Information:
PubMed ID: 25438330
INSPEC Accession Number: 15685175
Publisher: IEEE

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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 imbalanced characteristics of medical data from EHRs, data analysis task faces a big challenge. In this paper, we address the challenges of imbalanced medical data about a brain tumor diagnosis problem. Morphometric analysis of histopathological images is rapidly emerging as a valuable diagnostic tool for neuropathology. Oligodendroglioma is one type of brain tumor that has a good response to treatment provided the tumor subtype is recognized accurately. The genetic variant, 1p-/19q-, has recently been found to have high chemosensitivity, and has morphological attributes that may lend it to automated image analysis and histological processing and diagnosis. This paper aims to achieve a fast, affordable, and objective diagnosis of this genetic variant of oligodendroglioma with a novel data mining approach combining a feature selection and ensemble-based classification. In this paper, 63 instances of brain tumor with oligodendroglioma are obtained due to prevalence and incidence of the tumor variant. In order to minimize the effect of an imbalanced healthcare data set, a global optimization-based hybrid wrapper-filter feature selection with ensemble classification is applied. The experiment results show that the proposed approach outperforms the standard techniques used in brain tumor classification problem to overcome the imbalanced characteristics of medical data.
Published in: IEEE Access ( Volume: 4 )
Date of Publication: 2016
Online ISSN: 2169-3536
INSPEC Accession Number: 16606863
Publisher: IEEE
Sponsored by: IEEE

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