Analysis of Classifiers for Prediction of Type II Diabetes Mellitus
- Barhate, Rahul, Kulkarni, Pradnya
- Authors: Barhate, Rahul , Kulkarni, Pradnya
- Date: 2018
- Type: Text , Conference proceedings , Conference paper
- Relation: 4th International Conference on Computing, Communication Control and Automation, ICCUBEA 2018
- Full Text:
- Reviewed:
- Description: Diabetes mellitus is a chronic disease and a health challenge worldwide. According to the International Diabetes Federation, 451 million people across the globe have diabetes, with this number anticipated to rise up to 693 million people by 2045. It has been shown that 80% of the complications arising from type II diabetes can be prevented or delayed by early identification of the people who are at risk. Diabetes is difficult to diagnose in the early stages as its symptoms grow subtly and gradually. In a majority of the cases, the patients remain undiagnosed until they are admitted for a heart attack or begin to lose their sight. This paper analyzes the different classification algorithms based on a patient's health history to aid doctors identify the presence of as well as promote early diagnosis and treatment. The experiments were conducted on Pima Indian Diabetes data set. Various classifiers used include K Nearest Neighbors, Logistic Regression, Decision Trees, Random Forest, Gradient Boosting, Support Vector Machine and Neural Network. Results demonstrate that Random Forests performed well on the data set giving an accuracy of 79.7%. © 2018 IEEE.
- Description: E1
- Authors: Barhate, Rahul , Kulkarni, Pradnya
- Date: 2018
- Type: Text , Conference proceedings , Conference paper
- Relation: 4th International Conference on Computing, Communication Control and Automation, ICCUBEA 2018
- Full Text:
- Reviewed:
- Description: Diabetes mellitus is a chronic disease and a health challenge worldwide. According to the International Diabetes Federation, 451 million people across the globe have diabetes, with this number anticipated to rise up to 693 million people by 2045. It has been shown that 80% of the complications arising from type II diabetes can be prevented or delayed by early identification of the people who are at risk. Diabetes is difficult to diagnose in the early stages as its symptoms grow subtly and gradually. In a majority of the cases, the patients remain undiagnosed until they are admitted for a heart attack or begin to lose their sight. This paper analyzes the different classification algorithms based on a patient's health history to aid doctors identify the presence of as well as promote early diagnosis and treatment. The experiments were conducted on Pima Indian Diabetes data set. Various classifiers used include K Nearest Neighbors, Logistic Regression, Decision Trees, Random Forest, Gradient Boosting, Support Vector Machine and Neural Network. Results demonstrate that Random Forests performed well on the data set giving an accuracy of 79.7%. © 2018 IEEE.
- Description: E1
Pixel N-grams for Mammographic Image Classification
- Authors: Kulkarni, Pradnya
- Date: 2017
- Type: Text , Thesis , PhD
- Full Text:
- Description: X-ray screening for breast cancer is an important public health initiative in the management of a leading cause of death for women. However, screening is expensive if mammograms are required to be manually assessed by radiologists. Moreover, manual screening is subject to perception and interpretation errors. Computer aided detection/diagnosis (CAD) systems can help radiologists as computer algorithms are good at performing image analysis consistently and repetitively. However, image features that enhance CAD classification accuracies are necessary for CAD systems to be deployed. Many CAD systems have been developed but the specificity and sensitivity is not high; in part because of challenges inherent in identifying effective features to be initially extracted from raw images. Existing feature extraction techniques can be grouped under three main approaches; statistical, spectral and structural. Statistical and spectral techniques provide global image features but often fail to distinguish between local pattern variations within an image. On the other hand, structural approach have given rise to the Bag-of-Visual-Words (BoVW) model, which captures local variations in an image, but typically do not consider spatial relationships between the visual “words”. Moreover, statistical features and features based on BoVW models are computationally very expensive. Similarly, structural feature computation methods other than BoVW are also computationally expensive and strongly dependent upon algorithms that can segment an image to localize a region of interest likely to contain the tumour. Thus, classification algorithms using structural features require high resource computers. In order for a radiologist to classify the lesions on low resource computers such as Ipads, Tablets, and Mobile phones, in a remote location, it is necessary to develop computationally inexpensive classification algorithms. Therefore, the overarching aim of this research is to discover a feature extraction/image representation model which can be used to classify mammographic lesions with high accuracy, sensitivity and specificity along with low computational cost. For this purpose a novel feature extraction technique called ‘Pixel N-grams’ is proposed. The Pixel N-grams approach is inspired from the character N-gram concept in text categorization. Here, N number of consecutive pixel intensities are considered in a particular direction. The image is then represented with the help of histogram of occurrences of the Pixel N-grams in an image. Shape and texture of mammographic lesions play an important role in determining the malignancy of the lesion. It was hypothesized that the Pixel N-grams would be able to distinguish between various textures and shapes. Experiments carried out on benchmark texture databases and binary basic shapes database have demonstrated that the hypothesis was correct. Moreover, the Pixel N-grams were able to distinguish between various shapes irrespective of size and location of shape in an image. The efficacy of the Pixel N-gram technique was tested on mammographic database of primary digital mammograms sourced from a radiological facility in Australia (LakeImaging Pty Ltd) and secondary digital mammograms (benchmark miniMIAS database). A senior radiologist from LakeImaging provided real time de-identified high resolution mammogram images with annotated regions of interests (which were used as groundtruth), and valuable radiological diagnostic knowledge. Two types of classifications were observed on these two datasets. Normal/abnormal classification useful for automated screening and circumscribed/speculation/normal classification useful for automated diagnosis of breast cancer. The classification results on both the mammography datasets using Pixel N-grams were promising. Classification performance (Fscore, sensitivity and specificity) using Pixel N-gram technique was observed to be significantly better than the existing techniques such as intensity histogram, co-occurrence matrix based features and comparable with the BoVW features. Further, Pixel N-gram features are found to be computationally less complex than the co-occurrence matrix based features as well as BoVW features paving the way for mammogram classification on low resource computers. Although, the Pixel N-gram technique was designed for mammographic classification, it could be applied to other image classification applications such as diabetic retinopathy, histopathological image classification, lung tumour detection using CT images, brain tumour detection using MRI images, wound image classification and tooth decay classification using dentistry x-ray images. Further, texture and shape classification is also useful for classification of real world images outside the medical domain. Therefore, the pixel N-gram technique could be extended for applications such as classification of satellite imagery and other object detection tasks.
- Description: Doctor of Philosophy
- Authors: Kulkarni, Pradnya
- Date: 2017
- Type: Text , Thesis , PhD
- Full Text:
- Description: X-ray screening for breast cancer is an important public health initiative in the management of a leading cause of death for women. However, screening is expensive if mammograms are required to be manually assessed by radiologists. Moreover, manual screening is subject to perception and interpretation errors. Computer aided detection/diagnosis (CAD) systems can help radiologists as computer algorithms are good at performing image analysis consistently and repetitively. However, image features that enhance CAD classification accuracies are necessary for CAD systems to be deployed. Many CAD systems have been developed but the specificity and sensitivity is not high; in part because of challenges inherent in identifying effective features to be initially extracted from raw images. Existing feature extraction techniques can be grouped under three main approaches; statistical, spectral and structural. Statistical and spectral techniques provide global image features but often fail to distinguish between local pattern variations within an image. On the other hand, structural approach have given rise to the Bag-of-Visual-Words (BoVW) model, which captures local variations in an image, but typically do not consider spatial relationships between the visual “words”. Moreover, statistical features and features based on BoVW models are computationally very expensive. Similarly, structural feature computation methods other than BoVW are also computationally expensive and strongly dependent upon algorithms that can segment an image to localize a region of interest likely to contain the tumour. Thus, classification algorithms using structural features require high resource computers. In order for a radiologist to classify the lesions on low resource computers such as Ipads, Tablets, and Mobile phones, in a remote location, it is necessary to develop computationally inexpensive classification algorithms. Therefore, the overarching aim of this research is to discover a feature extraction/image representation model which can be used to classify mammographic lesions with high accuracy, sensitivity and specificity along with low computational cost. For this purpose a novel feature extraction technique called ‘Pixel N-grams’ is proposed. The Pixel N-grams approach is inspired from the character N-gram concept in text categorization. Here, N number of consecutive pixel intensities are considered in a particular direction. The image is then represented with the help of histogram of occurrences of the Pixel N-grams in an image. Shape and texture of mammographic lesions play an important role in determining the malignancy of the lesion. It was hypothesized that the Pixel N-grams would be able to distinguish between various textures and shapes. Experiments carried out on benchmark texture databases and binary basic shapes database have demonstrated that the hypothesis was correct. Moreover, the Pixel N-grams were able to distinguish between various shapes irrespective of size and location of shape in an image. The efficacy of the Pixel N-gram technique was tested on mammographic database of primary digital mammograms sourced from a radiological facility in Australia (LakeImaging Pty Ltd) and secondary digital mammograms (benchmark miniMIAS database). A senior radiologist from LakeImaging provided real time de-identified high resolution mammogram images with annotated regions of interests (which were used as groundtruth), and valuable radiological diagnostic knowledge. Two types of classifications were observed on these two datasets. Normal/abnormal classification useful for automated screening and circumscribed/speculation/normal classification useful for automated diagnosis of breast cancer. The classification results on both the mammography datasets using Pixel N-grams were promising. Classification performance (Fscore, sensitivity and specificity) using Pixel N-gram technique was observed to be significantly better than the existing techniques such as intensity histogram, co-occurrence matrix based features and comparable with the BoVW features. Further, Pixel N-gram features are found to be computationally less complex than the co-occurrence matrix based features as well as BoVW features paving the way for mammogram classification on low resource computers. Although, the Pixel N-gram technique was designed for mammographic classification, it could be applied to other image classification applications such as diabetic retinopathy, histopathological image classification, lung tumour detection using CT images, brain tumour detection using MRI images, wound image classification and tooth decay classification using dentistry x-ray images. Further, texture and shape classification is also useful for classification of real world images outside the medical domain. Therefore, the pixel N-gram technique could be extended for applications such as classification of satellite imagery and other object detection tasks.
- Description: Doctor of Philosophy
Visual character N-grams for classification and retrieval of radiological images
- Kulkarni, Pradnya, Stranieri, Andrew, Kulkarni, Siddhivinayak, Ugon, Julien, Mittal, Manish
- Authors: Kulkarni, Pradnya , Stranieri, Andrew , Kulkarni, Siddhivinayak , Ugon, Julien , Mittal, Manish
- Date: 2014
- Type: Text , Journal article
- Relation: International Journal of Multimedia & Its Applications Vol. 6, no. 2 (April 2014), p. 35-49
- Full Text:
- Reviewed:
- Description: Diagnostic radiology struggles to maintain high interpretation accuracy. Retrieval of past similar cases would help the inexperienced radiologist in the interpretation process. Character n-gram model has been effective in text retrieval context in languages such as Chinese where there are no clear word boundaries. We propose the use of visual character n-gram model for representation of image for classification and retrieval purposes. Regions of interests in mammographic images are represented with the character n-gram features. These features are then used as input to back-propagation neural network for classification of regions into normal and abnormal categories. Experiments on miniMIAS database show that character n-gram features are useful in classifying the regions into normal and abnormal categories. Promising classification accuracies are observed (83.33%) for fatty background tissue warranting further investigation. We argue that Classifying regions of interests would reduce the number of comparisons necessary for finding similar images from the database and hence would reduce the time required for retrieval of past similar cases.
- Authors: Kulkarni, Pradnya , Stranieri, Andrew , Kulkarni, Siddhivinayak , Ugon, Julien , Mittal, Manish
- Date: 2014
- Type: Text , Journal article
- Relation: International Journal of Multimedia & Its Applications Vol. 6, no. 2 (April 2014), p. 35-49
- Full Text:
- Reviewed:
- Description: Diagnostic radiology struggles to maintain high interpretation accuracy. Retrieval of past similar cases would help the inexperienced radiologist in the interpretation process. Character n-gram model has been effective in text retrieval context in languages such as Chinese where there are no clear word boundaries. We propose the use of visual character n-gram model for representation of image for classification and retrieval purposes. Regions of interests in mammographic images are represented with the character n-gram features. These features are then used as input to back-propagation neural network for classification of regions into normal and abnormal categories. Experiments on miniMIAS database show that character n-gram features are useful in classifying the regions into normal and abnormal categories. Promising classification accuracies are observed (83.33%) for fatty background tissue warranting further investigation. We argue that Classifying regions of interests would reduce the number of comparisons necessary for finding similar images from the database and hence would reduce the time required for retrieval of past similar cases.
Pixel N-grams for mammographic lesion classification
- Kulkarni, Pradnya, Stranieri, Andrew, Ugon, Julien, Mittal, Manish, Kulkarni, Siddhivinayak
- 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.
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