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.
Semi-invasive system for detecting and monitoring dementia patients
- Authors: Yamsanwar, Yash , Patankar, Amol , Kulkarni, Siddhivinayak , Stratton, David , Stranieri, Andrew
- Date: 2019
- Type: Text , Conference proceedings , Conference paper
- Relation: 5th IEEE International Conference for Convergence in Technolog, I2CT 2019
- Full Text: false
- Reviewed:
- Description: Dementia is one of the most prevalent conditions faced by the elderly caused by specific brain cell damage. Various effects of dementia include a loss of memory, reduction in problem solving ability, analytical skills, and decision making capability. Few systems have been developed for the early detection of dementia. Existing systems depend largely on hardware e.g. sensors, gateways. Factors like maintainability and sustainability compromise the efficiency of such systems. This paper presents a novel approach towards the early detection of dementia and aims at eliminating some of the challenges posed by these systems. It also provides a comparati ve study of the cognitive abilities of healthy old-age people and those afflicted by dementia. © 2019 IEEE.
- Description: E1