Browsing by Author "Silva, C"

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Embargo
Mammogram-Based Cancer Detection Using Deep Convolutional Neural Network
(IEEE, 2021-12-02) Thudawehewa, H; Silva, C; Rathnayake, p; Thudawehewa, T
Breast cancer is now a common health problem among most women. Breast cancer is the world’s second-largest cause of mortality for women, and it affects mostly women over the age of 50. The major reasons are that most women do not have proper knowledge about breast diseases/conditions, and the inability to detect abnormalities in the initial stages. A mammogram is one of the best imaging modalities recommended by doctors to diagnose breast cancers. Consultant radiologists are necessary for the identification of those breast pathologies by mammogram images. For a human, it takes some time to read and have an opinion about the condition. Also, the pandemic situation makes the diagnosis processes even more difficult due to the unavailability of doctors and other medical staff. Deep learning approaches are applied for breast cancer detection, and it helps radiologists to identify breast pathologies quickly and accurately. In this work, the mammogram images are collected using MIAS, DDSM, and INbreast databases. The proposed system identifies the location of the lump within the breast, if the lump is malignant or benign, the size of the lump, and the state of the nipple (It is abnormal or not). Convolutional Neural Network (CNN) method for classifying screening mammograms obtained outstanding performance compared to the previous methods. This CNN method produces 96.5% accuracy for breast tumor classification and produces the 80% accuracy for nipple classification.
Embargo
Mammogram-Based Cancer Detection Using Deep Convolutional Neural Network
(IEEE, 2021-12-02) Thudawehewa, H; Rathnayake, P.; Thudawehewa, T; Silva, C
Breast cancer is now a common health problem among most women. Breast cancer is the world’s second-largest cause of mortality for women, and it affects mostly women over the age of 50. The major reasons are that most women do not have proper knowledge about breast diseases/conditions, and the inability to detect abnormalities in the initial stages. A mammogram is one of the best imaging modalities recommended by doctors to diagnose breast cancers. Consultant radiologists are necessary for the identification of those breast pathologies by mammogram images. For a human, it takes some time to read and have an opinion about the condition. Also, the pandemic situation makes the diagnosis processes even more difficult due to the unavailability of doctors and other medical staff. Deep learning approaches are applied for breast cancer detection, and it helps radiologists to identify breast pathologies quickly and accurately. In this work, the mammogram images are collected using MIAS, DDSM, and INbreast databases. The proposed system identifies the location of the lump within the breast, if the lump is malignant or benign, the size of the lump, and the state of the nipple (It is abnormal or not). Convolutional Neural Network (CNN) method for classifying screening mammograms obtained outstanding performance compared to the previous methods. This CNN method produces 96.5% accuracy for breast tumor classification and produces the 80% accuracy for nipple classification.
Embargo
Non-Communicable Diseases Detection System
(IEEE, 2021-12-09) Thudawehewa, H. R; Jayawardhana, W. A. P. T; Wellehewa, C. G; Silva, C; Rathnayake, P
This research paper presents a Non-communicable Diseases Detection System which is a centralized medical system designed for general public usage. The system aims to provide help for people with non-communicable diseases. In a pandemic situation like this where people find it difficult to reach medical facilities and staff, the system is more advantageous. The system covers areas related to the medical report analysis, BMI value prediction, and breast cancer analysis related to non-communicable diseases. Presently health reports are taken for every disease. BMI is a factor essential to everyone to lead a healthy life. The majority of women suffer from breast cancer. As per the findings of the report, the report analysis predicts possible diseases that can occur in the person concerned. In BMI prediction, particularly the possible BMI value and weight value for the next month is predicted. In Mammogram detection, it gives the current status of the breast. The report analysis model has 90.6% accuracy while the BMI prediction model has 99.7% accuracy. The mammogram detection model proved that it has 96.5% accuracy. All the aforesaid procedures were carried out by analyzing related data systematically. Machine learning, Deep learning, and Image processing techniques were used to develop this system. The main purpose of this system is to make the persons aware of their current health status and to prevent them from having non-communicable diseases.
Open Access
Source Code based Approaches to Automate Marking in Programming Assignments
(Science and Technology Publications, 2021) Kuruppu, T; Tharmaseelan, J; Silva, C; Samaratunge Arachchillage, U. S. S; Manathunga, K; Reyal, S; Kodagoda, N; Jayalath, T
With the embarkment of this technological era, a significant demand over programming modules can be observed among university students in larger volume. When figures grow exponentially, manual assessments and evaluations would be a tedious and error-prone activity, thus marking automation has become fast growing necessity. To fulfil this objective, in this review paper, authors present literature on automated assessment of coding exercises, analyse the literature from four dimensions as Machine Learning approaches, Source Graph Generation, Domain Specific Languages, and Static Code Analysis. These approaches are reviewed on three main aspects: accuracy, efficiency, and user-experience. The paper finally describes a series of recommendations for standardizing the evaluation and benchmarking of marking automation tools for future researchers to obtain a strong empirical footing on the domain, thereby leading to further advancements in the field.
Open Access
Source Code based Approaches to Automate Marking in Programming Assignments.
(Science and Technology Publications, 2021) Kuruppu, T; Tharmaseelan, J; Silva, C; Samaratunge Arachchillage, U. S. S; Manathunga, K; Reyal, S; Kodagoda, N
With the embarkment of this technological era, a significant demand over programming modules can be observed among university students in larger volume. When figures grow exponentially, manual assessments and evaluations would be a tedious and error-prone activity, thus marking automation has become fast growing necessity. To fulfil this objective, in this review paper, authors present literature on automated assessment of coding exercises, analyse the literature from four dimensions as Machine Learning approaches, Source Graph Generation, Domain Specific Languages, and Static Code Analysis. These approaches are reviewed on three main aspects: accuracy, efficiency, and user-experience. The paper finally describes a series of recommendations for standardizing the evaluation and benchmarking of marking automation tools for future researchers to obtain a strong empirical footing on the domain, thereby leading to further advancements in the field.