Publications

Computational Biology

 Bioinformatics Workflow Modelling and Analysis

• •  A. Welivita, I. Perera, D. Meedeniya, "An Interactive Workflow Generator to Support Bioinformatics Analysis through GPU Acceleration", The IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE Xplore, Kansas City, MO, USA, 2017, pp. 457-462. DOI:  https://doi.org/10.1109/BIBM.2017.8217691

  •  A. Welivita, I. Perera, D. Meedeniya, A. Wickramarachchi and V. Mallawaarachchi, "Managing Complex Workflows in Bioinformatics - An Interactive Toolkit with GPU Acceleration," in IEEE Transactions on NanoBioscience, vol. 17, no. 3, pp. 199-208, July 2018. DOI:  https://doi.org/10.1109/TNB.2018.2837122

  •  V. Mallawaarachchi, A. Wickaramarachchi, A. Weliwita, I. Perera, D. Meedeniya, Experiential Learning in Bioinformatics – Learner Support for Complex Workflow Modelling and Analysis, International Journal of Emerging Technologies in Learning (iJET), Vol 13, No 12, pp. 19-34, 2018. DOI: https://doi.org/10.3991/ijet.v13i12.8608

  •  D. Rathnayake, A. Wickramarachchi, V. Mallawaarachchi, D. Meedeniya, I. Perera, “A Realtime Monitoring Platform for Workflow Subroutines”, 18th International Conference on Advances in ICT for Emerging Regions (ICTer), IEEE explorer, 2018, Colombo, pp. 41-47. DOI: https://doi.org/10.1109/ICTER.2018.8615557 

Genomic Data Extraction and Visualization

• U. Maduranga, K. Wijegunarathna, S. Weerasinghe, I. Perera and A. Wickramarachchi, "Dimensionality Reduction for Cluster Identification in Metagenomics using Autoencoders," 2020 20th International Conference on Advances in ICT for Emerging Regions (ICTer), Colombo, Sri Lanka, 2020, pp. 113-118, DOI: https://doi.org/10.1109/ICTer51097.2020.9325447.

• K. Wijegunarathna, U. Maduranga, S. Weerasinghe, I. Perera and A. Wickramarachchi, "MetaG: a comprehensive visualization tool to explore metagenomes," 2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Seoul, Korea (South), 2020, pp. 460-463, DOI: https://doi.org/10.1109/BIBM49941.2020.9313166.

• K. Wijegunarathna, U. Maduranga, S. Weerasinghe, I. Perera and A. Wickramarachchi, Cluster Identification in Metagenomics – A Novel Technique of Dimensionality Reduction through Autoencoders. International Journal on Advances in ICT for Emerging Regions (ICTer), 14(2), pp.9–18, 2021. DOI: http://doi.org/10.4038/icter.v14i2.7224

Metagenomics 

• M. Piumini, S. Liyanage, T. Bogahawaththa, I. Perera and V. Mallawaarachchi, "EasyBin: Metagenomics Binning Services for Non-Specialists," 2021 Moratuwa Engineering Research Conference (MERCon), Moratuwa, Sri Lanka, 2021, pp. 687-692, doi: 10.1109/MERCon52712.2021.9525708.

• S. Chandrasiri, T. Perera, A. Dilhara, I. Perera, V. Mallawaarachchi, "CH-Bin: A Convex Hull Based Approach for Binning Metagenomic Contigs", Computational Biology and Chemistry, 2022,107734,DOI:  https://doi.org/10.1016/j.compbiolchem.2022.107734.

• C. Nandasiri, S. Alahakoon, G. Dassanayake, A. Wickramarachchi and I. Perera, "MetaPCbin: Plasmid/Chromosome Classification for Metagenomic Contigs using Machine Learning Techniques," 2022 Moratuwa Engineering Research Conference (MERCon), 2022, pp. 1-6, doi: https://doi.org/10.1109/MERCon55799.2022.9906214. 

• S. Alahakoon, G. Dassanayake, C. Nandasiri, A. Wickramarachchi and I. Perera, "MetaGraph: Plasmid/Chromosome Classification Enhancement Using Graph Neural Networks," 2022 Moratuwa Engineering Research Conference (MERCon), 2022, pp. 1-6, doi: https://doi.org/10.1109/MERCon55799.2022.9906285. 

Tool Support for Bioinformatics Learning

• • A. Wickramarachchi, V. Mallawaarachchi, D. Meedeniya, I. Perera, A. Waliwita “Enhanced Student Learning in Proteomics - An Interactive Tool Support for Teaching Workflows”, 7th IEEE International Conference on Teaching, Assessment and Learning for Engineering (TALE), IEEE explorer, 2018, NSW, Australia, pp. 228-235. DOI: https://doi.org/10.1109/TALE.2018.8615134

Service Support for Bioinformatics

• •  V. Mallawaarachchi, A. Wickramarachchi, A. Welivita, I. Perera, and D. Meedeniya, "Efficient Bioinformatics Computations through GPU Accelerated Web Services", In Proceedings of the 2018 2nd International Conference on Algorithms, Computing and Systems (ICACS '18), ACM, New York, USA, 2018, pp. 94-98. DOI: https://doi.org/10.1145/3242840.3242848

  •  S. Prasadi, V. Mallawaarachchi, A. Wickramarachchi, I. Perera, D. Meedeniya, “Efficient Scheduling for Scalable Bioinformatics Analysis Platform with Microservices”, 18th International Conference on Advances in ICT for Emerging Regions (ICTer), IEEE explorer, 2018, Colombo, pp. 400-406. DOI: https://doi.org/10.1109/ICTER.2018.8615599

  • S. Rajapaksa, A. Wickramarachchi, V. Mallawaarachchi, W. Rasanjana, I. Perera and D. Meedeniya, "A Scalable Bioinformatics Analysis Platform based on Microservices Architecture," International Research Conference on Smart Computing and Systems Engineering (SCSE), pp. 70-77. Sri Lanka, 2019. DOI: https://doi.org/10.23919/SCSE.2019.8842809. 

  •  D. Lenadora, A. Wickramarachchi, V. Mallawaarachchi, I Perera, D. Meedniya, “An Adapter Architecture for Heterogeneous Data Processing in Bioinformatics Pipelines“ , Moratuwa Engineering Research Conference (MERCon), IEEE explorer, Sri Lanka, 2019. pp. 692-697. DOI = http://dx.doi.org/10.1109/MERCon.2019.8818781  (Best paper award - Software Engineering Track )

 Interactive Game play

• •  D. A. Meedeniya, S. A. P. A. Rukshan, A. Welivita, “An Interactive Gameplay to Crowdsource Multiple Sequence Alignment of Genome Sequences: Genenigma”, 9th International Conference on Bioscience, Biochemistry and Bioinformatics (ICBBB 2019), ACM, NY, Singapore, 2019, pp.   . DOI: https://doi.org/10.1145/3314367.3314374

  • G. Gamage,  I. Perera, D. Meedeniya, Anuradha Welivita, “A Crowdsourced Gameplay for Whole-Genome Assembly via Short Reads”. International Journal of Online and Biomedical Engineering (iJOE), Vol.16, No.8,  pp. 68-84, 2020. DOI: https://doi.org/10.3991/ijoe.v16i08.14821 

 Phylogeny

• •  S. Rajapaksa, W. Rasanjana, I. Perera, D. Meedeniya, “GPU Accelerated Maximum Likelihood Analysis for Phylogenetic Inference”, 8th International Conference on Software and Computer Applications (ICSCA 2019), ACM, New York, USA, 2019, pp. 6-10, Malaysia. DOI: https://doi.org/10.1145/3316615.3316630

  •  O. G. Samarasinghe, J.A.C.G. Jathunarachchi, H.M.D. Jeewanthi, D. A. Meedeniya, S. Rajapaksa, “Rule-Based Recommendation System for Phylogenetic Inference”, Moratuwa Engineering Research Conference (MERCon), IEEE explorer, Sri Lanka, 2019. pp.  704-709. DOI = http://dx.doi.org/10.1109/MERCon.2019.8818878  

  • G. Gamage, N. Gimhana , A. Wickramarachchi , V. Mallawaarachchi , I. Perera , "Alignment-free Whole Genome Comparison Using k-mer Forests", 19th International Conference on Advances in ICT for Emerging Regions (ICTer), Colombo, Sri Lanka, 2019, pp. 1-7. DOI: http://dx.doi.org/10.1109/icter48817.2019.9023714 . (Best paper award - Bioinformatics, Image Processing and NLP track)

  • G. Gamage, N. Gimhana, I. Perera, S. Bandara, T. Pathirana, A. Wickramarachchi, V. Mallawaarachchi, “Phylogenetic tree construction using k-mer forest-based distance calculation,” International Journal of Online and Biomedical Engineering (iJOE), Vol.16, No.7, pp.. 4-20, 2020. DOI: https://doi.org/10.3991/ijoe.v16i07.13807

Biomedical Imaging and Computational Modelling

Neurological disorder identification using psychophysiological data

• •  S. De Silva, S. Dayarathna, G. Ariyarathne, D. Meedeniya, S. Jayarathna, A. M. P. Michalek, G. Jayawardena, “A Rule-Based System for ADHD Identification using Eye Movement Data”, Moratuwa Engineering Research Conference (MERCon), IEEE explorer, Sri Lanka, 2019. pp.  538-543. DOI = http://dx.doi.org/10.1109/MERCon.2019.8818865  (Best paper award - Biomedical Engineering and Instrumentation and Technology Management Track )

  • D. Haputhanthri, G. Brihadiswaran, S. Gunathilaka, D. Meedeniya, S. Jayarathna, M. Jaime, Y. Jayawardena, “An EEG based Channel Optimized Classification Approach for Autism Spectrum Disorder”, Moratuwa Engineering Research Conference (MERCon), IEEE explorer, Sri Lanka, 2019. pp. 123-128. DOI = http://dx.doi.org/10.1109/MERCon.2019.8818814  

  • S. De Silva, S. Dayarathna, G. Ariyarathne, D. Meedeniya, S. Jayarathna, “A Survey of Attention Deficit Hyperactivity Disorder Identification Using Psychophysiological Data”, International Journal of Online and Biomedical Engineering (iJOE), vol. 15, no. 13, pp. 61-76  , 2019. DOI: https://doi.org/10.3991/ijoe.v15i13.10744   

  • G. Brihadiswaran, D. Haputhanthri, S. Gunathilaka, D. Meedeniya, S. Jayarathna, “A Review of EEG-based Classification for Autism Spectrum Disorder”, Journal of Computer Science (JCS), vol.15, no.8, pp. 1161.1183, 2019. DOI: http://dx.doi.org/10.3844/jcssp.2019.1161.1183   

  • I. D. Rubasinghe, D. A. Meedeniya, “Automated Neuroscience Decision Support Framework”, B. Agarwal (Eds), in Deep Learning Techniques for Biomedical and Health Informatics, Chapter 13, pp. 305-326, Elsevier, Academic Press, 2020. ISBN: 978-0-12-819061-6. DOI: https://doi.org/10.1016/B978-0-12-819061-6.00013-6

  • D. A. Meedeniya, I.D. Rubasinghe, “A Review of Supportive Computational Approaches for Neurological Disorder Identification”, T. Wadhera, D. Kakkar (Eds.), in Interdisciplinary Approaches to Altering Neurodevelopmental Disorders, Chapter 16, pp. 271-302, IGI Gloabal, 2020. SBN13: 9781799830696|ISBN10: 1799830691|EISBN13: 9781799830702 DOI: https://doi.org/10.4018/978-1-7998-3069-6.ch016 

  • G. Ariyarathne, S. De Silva, S. Dayarathna, , D. Meedeniya, S. Jayarathna, “ADHD Identification using Convolutional Neural Network with Seed-based Approach for fMRI Data”, 9th International Conference on Software and Computer Applications (ICSCA), pp. 31-35, ACM, 2020. https://doi.org/10.1145/3384544.3384552 . pdf   (Presentation) 

  • D. Haputhanthri, G. Brihadiswaran, S. Gunathilaka, D. Meedeniya, S. Jayarathna, M. Jaime, C. Harshaw, Integration of Facial Thermography in EEG-based Classification of ASD. International Journal of Automation and Computi, 17(6), pp. 837-854, December 2020. DOI: https://doi.org/10.1007/s11633-020-1231-6

  • S. De Silva, S. Dayarathna, G. Ariyarathne, D. Meedeniya, S. Jayarathna, “fMRI Feature Extraction Model for ADHD Classification Using Convolutional Neural Network“, International Journal of E-Health and Medical Communications (IJEHMC) , 12:1 (6), pp. 81-105, IGI Global, 2021.  https://doi.org/10.4018/IJEHMC.2021010106

  • S. De Silva, S. Dayarathna, G. Ariyarathne, D. Meedeniya, S. Jayarathna, A. M. P. Michalek, "Computational Decision Support System for ADHD Identification", International Journal of Automation and Computing (IJAC), vol.18, no.2, pp.233–255, 2021.  DOI:  https://doi.org/10.1007/s11633-020-1252-1   (pdf) 

  • L. Herath, D. Meedeniya, M.A.J.C Marasingha, V. Weerasinghe, "Autism Spectrum Disorder Diagnosis Support Model Using InceptionV3", International Research Conference on Smart Computing and Systems Engineering (SCSE), Kelaniya, Sri Lanka, 2021, pp. 1-7. DOI: https://doi.org/10.1109/SCSE53661.2021.9568314  (Best paper award) (presentation)

  • L. Herath, D. Meedeniya, J. Marasingha and V. Weerasinghe, "Optimize Transfer Learning for Autism Spectrum Disorder Classification with Neuroimaging: A Comparative Study," 2nd International Conference on Advanced Research in Computing (ICARC), Belihuloya, Sri Lanka, 2022, pp. 171-176, DOI: http://dx.doi.org/10.1109/ICARC54489.2022.9753949. .  (Best paper award: ‘Data Science and Applications Track’) (pdf) (Presentation) 

  • S. de Silva, S. Dayarathna, & D. Meedeniya, Alzheimer’s Disease Diagnosis using Functional and Structural Neuroimaging Modalities, Wadhera, T., & Kakkar, D. (Eds), in Enabling Technology for Neurodevelopmental Disorders: from Diagnosis to Rehabilitation, Ch. 11, pp. 162—183, Routledge, Taylor & Francis CRS Press, 2022.  DOI: http://dx.doi.org/10.4324/9781003165569-11  eBook ISBN 9781003165569; pdf 

Nerve Segmentation

• • I. D. Rubasinghe, D. A. Meedeniya, “Ultrasound Nerve Segmentation Using Deep Probabilistic Programming”, Journal of ICT Research and Applications, vol. 13, no. 3, pp. 241-256 , 2019, ITB Journal Publisher, ISSN: 2337-5787, DOI: https://doi.org/10.5614/itbj.ict.res.appl.2019.13.3.5. 

Computational Cancer Biology

• • W. Rasanjana, S. Rajapaksa, I. Perera and D. Meedeniya, A SVM Model for Candidate Y-chromosome Gene Discovery in Prostate Cancer, In: Oliver Eulenstein, Hisham Al-Mubaid and Qin Ding (editors). Proceedings of 11th International Conference on Bioinformatics and Computational Biology (BICOB 2019), vol 60, pages 129--138, March 18, 2019, Hawaii, USA. DOI: https://doi.org/10.29007/3nzw 

  • N. Wijethilake, M. Islam, D. Meedeniya, C. Chitraranjan, I. Perera and H. Ren, Radiogenomics of Glioblastoma: Identification of Radiomics associated with Molecular Subtypes, In: Kia S.M. et al. (eds) Machine Learning in Clinical Neuroimaging and Radiogenomics in Neuro-oncology, 2nd MICCAI workshop on Radiomics and Radiogenomics in Neuro-oncology using AI (MLCN), RNO-AI 2020. Lecture Notes in Computer Science, vol 12449,  Springer, Cham, pp 229-239, 2020. https://doi.org/10.1007/978-3-030-66843-3_22 (pdf). (Presentation) 

  •  N. Wijethilake, D. Meedeniya, C. Chitraranjan, I. Perera, “Survival prediction and risk estimation of Glioma patients using mRNA expressions”, 20th IEEE Conference on Bioinformatics and Bioengineering (BIBE), Cincinnati, OH, USA, 2020, pp. 35-42, DOI: https://doi.org/10.1109/BIBE50027.2020.00014. pdf (Presentation) 

  • N. Wijethilake, D. Meedeniya, C. Chitraranjan, I. Perera, M. Islam and H. Ren, "Glioma Survival Analysis Empowered with Data Engineering - A Survey", IEEE Access, vol. 9, pp. 43168-43191, 2021. DOI: https://doi.org/10.1109/ACCESS.2021.3065965

  • S. Dasanayaka, S. Silva, V. Shantha, D. Meedeniya and T. Ambegoda, "Interpretable Machine Learning for Brain Tumor Analysis Using MRI," 2nd International Conference on Advanced Research in Computing (ICARC), Belihuloya, Sri Lanka, 2022, pp. 212-217, DOI: http://dx.doi.org/10.1109/ICARC54489.2022.9754131. (pdf) (Presentation) 

  • S. Dasanayaka, V. Shantha, S. Anupa Silva, D. Meedeniya and T. Ambegoda, "Interpretable Machine Learning for Brain Tumour Analysis using MRI and Whole Slide Images," Software Impacts, vol. 13, pp. 100340,, 2022, DOI:https://doi.org/10.1016/j.simpa.2022.100340 (pdf)

  • A.Hussaindeen, S. Iqbal, and T. Ambegoda. "Multi-Label Prototype Based Interpretable Machine Learning for Melanoma Detection." International Journal of Advances in Signal and Image Sciences, vol 8, no. 1, 2022, pp. 40-53. DOI: https://doi.org/10.29284/ijasis.8.1.2022.40-53

  • S. R. Dash, S. Roy, J. R. Mohanty, D. Meedeniya, M. R. Mishra,"Machine Learning Approach to Analyze Breast Cancer", 10th International Conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA), Mizoram, India, In: V. Bhateja, X. S. Yang, J. Chun-Wei Lin, R. Das, (eds), Intelligent Data Engineering and Analytics., Smart Innovation, Systems and Technologies, vol 327, Springer, pp. 387–394, 2023. DOI: https://doi.org/10.1007/978-981-19-7524-0_34 (pdf) 

  • L. Gamage, U. Isuranga, S. De Silva and D. Meedeniya, "Melanoma Skin Cancer Classification with Explainability," Proceedings of the 3rd International Conference on Advanced Research in Computing (ICARC), Belihuloya, Sri Lanka, 2023, pp. 30-35, doi:  https://doi.org/10.1109/ICARC57651.2023.10145622. (Pdf)  (Presentation) 

  • L. Gamage, U. Isuranga, D. Meedeniya, S. De Silva, and P. Yogarajah, "Melanoma Skin Cancer Identification with Explainability Utilizing Mask Guided Technique" Electronics 13, no. 4: 680. 2024. Doi: https://doi.org/10.3390/electronics13040680 

  • N. Gnanavel, P. Inparaj, N. Sritharan, D. Meedeniya and P. Yogarajah, "Interpretable Cervical Cell Classification: A Comparative Analysis," 2024 4th International Conference on Advanced Research in Computing (ICARC), Belihuloya, Sri Lanka, 2024, pp. 7-12, doi: https://doi.org/10.1109/ICARC61713.2024.10499737.

 Computational Biology of Diabetes

• •  H. V. L. C. Gamage, W. O. K. I. S. Wijesinghe, I. Perera, "Instance-based Segmentation for Boundary Detection of Neuropathic Ulcers through Mask-RCNN", In International Conference on Artificial Neural Networks (ICANN 2019),  Germany, Springer, pp. 511–522. DOI: https://doi.org/10.1007/978-3-030-30493-5_49 

  •  I. Wijesinghe, C. Gamage, I. Perera, C. Chitraranjan, "A Smart Telemedicine System with Deep Learning to Manage Diabetic Retinopathy and Foot Ulcers", Moratuwa Engineering Research Conference (MERCon), Sri Lanka, 2019, pp. 686-691. DOI: http://dx.doi.org/10.1109/MERCon.2019.8818682 

  • I. Wijesinghe, C. Gamage, C. Chitraranjan, "Transfer Learning with Ensemble Feature Extraction and Low-rank Matrix Factorization for Severity Stage Classification of Diabetic Retinopathy", In  31st International Conference on Tools with Artificial Intelligence (ICTAI), Portland, OR, USA, 2019, pp. 931-938, DOI: http://dx.doi.org/10.1109/ICTAI.2019.00132. 

  •  I. Wijesinghe, C. Gamage, C. Chitraranjan, "Deep Supervised Hashing through Ensemble CNN Feature Extraction and Low-rank Matrix Factorization for Retinal Image Retrieval of Diabetic Retinopathy", IEEE 19th International Conference on Bioinformatics and Bioengineering (BIBE), Athens, Greece, 2019, pp. 301-308. DOI: http://dx.doi.org/10.1109/BIBE.2019.00061 

  •  C. Gamage, I. Wijesinghe, I. Perera, "Automatic Scoring of Diabetic foot Ulcers through Deep CNN based Feature Extraction with Low Rank Matrix Factorization", IEEE 19th International Conference on Bioinformatics and Bioengineering (BIBE), Athens, Greece, 2019, pp. 352-356. DOI: http://dx.doi.org/10.1109/BIBE.2019.00069 

 Gastrointestinal Tract Research

• • C. Gamage, I. Wijesinghe, C. Chitraranjan, I. Perera, "GI-Net: Anomalies Classification in Gastrointestinal Tract through Endoscopic Imagery with Deep Learning", Moratuwa Engineering Research Conference (MERCon),  Sri Lanka, 2019, pp. 66-71. DOI: http://dx.doi.org/10.1109/MERCon.2019.8818929 

 Glaucoma Identification Research

• • T. Shyamalee and D. Meedeniya, "CNN Based Fundus Images Classification For Glaucoma Identification," 2nd International Conference on Advanced Research in Computing (ICARC), Belihuloya, Sri Lanka, 2022, pp. 200-205, DOI: http://dx.doi.org/10.1109/ICARC54489.2022.9754171. (pdf) (Presentation) 

  • T. Shyamalee and D. Meedeniya, "Attention U-Net for Glaucoma Identification Using Fundus Image Segmentation," 2022 International Conference on Decision Aid Sciences and Applications (DASA), Chiangrai, Thailand, 2022, pp. 6-10, doi: https://doi.org/10.1109/DASA54658.2022.9765303. (pdf) (Presentation)

  • T. Shyamalee and D. Meedeniya, “Glaucoma Detection with Retinal Fundus Images Using Segmentation and Classification”, Machine Intelligence Research, vol. 19, no. 6, pp. 563-580, 2022. DOI: http://doi.org/10.1007/s11633-022-1354-z (pdf)

  • T. Shyamalee, D. Meedeniya, G. Lim and M. Karunarathne, "Automated Tool Support for Glaucoma Identification With Explainability Using Fundus Images," in IEEE Access, vol. 12, pp. 17290-17307, 2024, doi: https://doi.org/10.1109/ACCESS.2024.3359698. 

Blood Pressure Related Research

• • B. Manamperi and C. Chitraranjan, "A Robust Neural Network-Based Method to Estimate Arterial Blood Pressure Using Photoplethysmography.," 2019 IEEE 19th International Conference on Bioinformatics and Bioengineering (BIBE), Athens, Greece, 2019, pp. 681-685, DOI: http://dx.doi.org/10.1109/BIBE.2019.00128 .

Respiratory Diseases  Analysis

• • S. Kolonne, H. Kumarasinghe, C. Fernando, D. Meedeniya, MobileNetV2 Based Chest X-Rays Classification, International Conference on Decision Aid Sciences and Application (DASA), Bahrain, 2021, pp. 57-61, DOI: http://doi.org/10.1109/DASA53625.2021.9682248   (pdf) (Presentation)

  • C. Fernando, S. Kolonne, H. Kumarasinghe and D. Meedeniya, "Chest Radiographs Classification Using Multi-model Deep Learning: A Comparative Study," 2nd International Conference on Advanced Research in Computing (ICARC), Belihuloya, Sri Lanka, 2022, pp. 165-170, DOI: http://dx.doi.org/10.1109/ICARC54489.2022.9753811. (pdf) (Presentation) 

  • K. A. S. H. Kumarasinghe,  S. L. Kolonne, K. C. M. Fernando, D. Meedeniya, "U-Net Based Chest X-ray Segmentation with Ensemble Classification for Covid-19 and Pneumonia", International Journal of Online and Biomedical Engineering (iJOE), Vol. 18, No. 7, pp. 161-174, 2022.  DOI: https://doi.org/10.3991/ijoe.v18i07.30807

  • D. Meedeniya, H. Kumarasinghe, S. Kolonne, C. Fernando, I. De la Torre Díez, G. Marques, “Chest X-ray Analysis Empowered with Deep Learning: A Systematic Review”, Applied Soft Computing, vol. 126, pp. 109319, 2022. DOI: https://doi.org/10.1016/j.asoc.2022.109319. (pdf)

  • V. Wijerathna, H. Raveen, S. Abeygunawardhana and T. D. Ambegoda, "Chest X-Ray Caption Generation with CheXNet," 2022 Moratuwa Engineering Research Conference (MERCon), 2022, pp. 1-6, doi: https://doi.org/10.1109/MERCon55799.2022.9906263. 

  • W. Nimalsiri, M. Hennayake, K. Rathnayake, T. D. Ambegoda and D. Meedeniya, "CXLSeg Dataset: Chest X-ray with Lung Segmentation," 2023 International Conference On Cyber Management And Engineering (CyMaEn), Bangkok, Thailand, 2023, pp. 327-331, doi:  https://doi.org/10.1109/CyMaEn57228.2023.10050951  (Presentation)

  • W. Indeewara, M. Hennayake, K. Rathnayake, T. Ambegoda, and D. Meedeniya, Chest X-ray Dataset with Lung Segmentation (version 1.0.0), PhysioNet, 2023, doi: https://doi.org/10.13026/9cy4-f535

  • W. Nimalsiri, M. Hennayake, K. Rathnayake, T. D. Ambegoda and D. Meedeniya, "Automated Radiology Report Generation Using Transformers," Proceedings of the 3rd International Conference on Advanced Research in Computing (ICARC), Belihuloya, Sri Lanka, 2023, pp. 90-95, doi:  https://doi.org/10.1109/ICARC57651.2023.10145699. (Pdf) (Best Paper Award) - Computer Vision and Image Processing Track  (Presentation) 

  • T. Wanasinghe, S. Bandara, S. Madusanka, D. Meedeniya, M. Bandara and I. d. l. T. Díez, "Lung Sound Classification with Multi-Feature Integration Utilizing Lightweight CNN Model," in IEEE Access, vol. 12, pp.  .  doi: https://doi.org/10.1109/ACCESS.2024.3361943. 

 Clinical Informatics

Adverse drug reactions

• • M. Y. Nilan, D. D. Sellahewa, A. S. R. Fernando, L. C. J. Gamage, and D. A Meedeniya “Analysis of Conflicts between Medication, Adverse Drug Reactions and Diseases”, IEEE 12th International Conference on Industrial and Information Systems (ICIIS), IEEE Xplore, Sri Lanka, 2017, pp. 1-6. DOI: https://doi.org/10.1109/ICIINFS.2017.8300339  

  •  Y. Nilan, D. Sellahewa, S. Fernando, L. Gamage and D. Meedeniya, " A Clinical Decision Support System with Identification of Drug Conflicts ", International Multidisciplinary Engineering Research Conference (MERCon2018), IEEE explorer, Sri Lanka, 2018. pp. 126-131. DOI: https://doi.org/10.1109/MERCon.2018.8422005 

  • K. Silva, T. Maheepala, K. Tharaka and T. D. Ambegoda, "Adversarial Learning to Improve Question Image Embedding in Medical Visual Question Answering," Moratuwa Engineering Research Conference (MERCon), 2022, pp. 1-6, doi: https://doi.org/10.1109/MERCon55799.2022.9906168. 

 Health-care Data Analytics

•  D.S. Lenadora and G.S.W. Gamage and H.D.I. Haputhanthri and D.  Meedeniya and I. Perera, "Exploratory Analysis of a Social Media Network in Sri Lanka during the  COVID-19 Virus Outbreak", 1-13, 2020, arXiv:2006.07855.  http://arxiv.org/abs/2006.07855

Assistive Technology for Special Needs

• C. Samarawickrama, D. Lenadora, R. Ranathunge, Y. De Silva, I. Perera & K. Welivita, Comic Based Learning for Students with Visual Impairments, International Journal of Disability, Development and Education, Taylor & Francis, pp. 1-19, 2021. DOI: 10.1080/1034912X.2021.1916893  (pdf)