Pixel N-grams for mammographic lesion classification
- Authors: Kulkarni, Pradnya , Stranieri, Andrew , Ugon, Julien , Mittal, Manish , Kulkarni, Siddhivinayak
- Date: 2017
- Type: Text , Conference proceedings
- Relation: 2017 2nd International Conference on Communication Systems, Computing and IT Applications, CSCITA , Mumbai; 7th-8th April, 2017; published in CSCITA 2017 - Proceedings p. 107-111
- Full Text: false
- Reviewed:
- Description: Automated classification algorithms have been applied to breast cancer diagnosis in order to improve the diagnostic accuracy and turnover time. However, classification accuracy, sensitivity and specificity could still be improved further. Moreover, reducing computational cost is another challenge as the number of images to be analyzed is typically large. In this paper, a novel Pixel N-gram approach inspired from character N-grams in the text retrieval context has been applied for mammographic lesion classification. The experiments on real world database demonstrate that the Pixel N-grams outperform the existing histogram as well as Haralick features with respect to classification accuracy as well as sensitivity. Effect of varying N and using various classifiers is also analyzed in this paper. Results show that optimum value of N is equal to 3 and MLP classifier performs better than SVM and KNN classifier using 3-gram features.
Texture image classification using pixel N-grams
- Authors: Kulkarni, Pradnya , Stranieri, Andrew , Ugon, Julien
- Date: 2016
- Type: Text , Conference proceedings
- Relation: 2016 IEEE International Conference on Signal and Image Processing (ICSIP); Beijing, China; 13-15 Aug, 2016 p. 137-141
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- Reviewed:
- Description: Various statistical methods such as co-occurrence matrix, local binary patterns and spectral approaches such as Gabor filters have been used for generating global features for image classification. However, global image features fail to distinguish between local variations within an image. Bag-of-visual-words (BoVW) model do capture local variations in an image, but typically do not consider spatial relationships between the visual words. Here, a novel image representation ‘Pixel N-grams’, inspired from the character N-gram concept in text retrieval has been applied for texture classification purpose. Texture is an important property for image classification. Experiments on the benchmark texture database (UIUC) demonstrates that the overall classification accuracy resulting from Pixel N-gram approach (89.5%) is comparable with that achieved using BoVW approach (84.4%) with the added advantage of simplicity and reduced computational cost.
An Agile group aware process beyond CRISP-DM: A hospital data mining case study
- Authors: Sharma, Vishakha , Stranieri, Andrew , Ugon, Julien , Martin, Laura
- Date: 2017
- Type: Text , Conference proceedings
- Relation: ICCDA '17: Proceedings of the International Conference on Computer and Data Analysis May 2017 p. 109-113
- Full Text: false
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- Description: The CRISP-DM methodology is commonly used in data analytics exercises within an organisation to provide system and structure to data mining processes. However, in providing a rigorous framework, CRISP-DM overlooks two facets of data analytics in organisational contexts; data mining exercises are far more agile and subject to change than presumed in CRISP-DM and central decisions regarding the interpretation of patterns discovered and the direction of analytics exercises are typically not made by individuals but by committees or groups within an organisation. The current study provides a case study of data mining in a hospital setting and suggests how the agile nature of an analytics exercise and the group reasoning inherent in key decisions can be accommodated within a CRISP-DM methodology.
Automatic sleep stage identification: difficulties and possible solutions
- Authors: Sukhorukova, Nadezda , Stranieri, Andrew , Ofoghi, Bahadorreza , Vamplew, Peter , Saleem, Muhammad Saad , Ma, Liping , Ugon, Adrien , Ugon, Julien , Muecke, Nial , Amiel, Hélène , Philippe, Carole , Bani-Mustafa, Ahmed , Huda, Shamsul , Bertoli, Marcello , Levy, P , Ganascia, J.G
- Date: 2010
- Type: Text , Conference proceedings
- Full Text:
- Description: The diagnosis of many sleep disorders is a labour intensive task that involves the specialised interpretation of numerous signals including brain wave, breath and heart rate captured in overnight polysomnogram sessions. The automation of diagnoses is challenging for data mining algorithms because the data sets are extremely large and noisy, the signals are complex and specialist's analyses vary. This work reports on the adaptation of approaches from four fields; neural networks, mathematical optimisation, financial forecasting and frequency domain analysis to the problem of automatically determing a patient's stage of sleep. Results, though preliminary, are promising and indicate that combined approaches may prove more fruitful than the reliance on a approach.