Data-driven solution to identify sentiments from online drug reviews. (2023)
Journal Article
HAQUE, R., LASKAR, S.H., KHUSHBU, K.G., HASAN, M.J. and UDDIN, J. 2023. Data-driven solution to identify sentiments from online drug reviews. Computers [online], 12(4), article 87. Available from: https://doi.org/10.3390/computers12040087

With the proliferation of the internet, social networking sites have become a primary source of user-generated content, including vast amounts of information about medications, diagnoses, treatments, and disorders. Comments on previously used medicin... Read More about Data-driven solution to identify sentiments from online drug reviews..

Contour extraction of medical images using an attention-based network. (2023)
Journal Article
LV, J.J., CHEN, H.Y., LI, J.W., LIN, K.H., CHEN, R.J., WANG, L.J., ZENG, X.X., REN, J.C. and ZHAO, H.M. 2023. Contour extraction of medical images using an attention-based network. Biomedical signal processing and control [online], 84, article 104828. Available from: https://doi.org/10.1016/j.bspc.2023.104828

A comprehensive analysis of medical images is important, as it assists in early screening and clinical treatment as well as subsequent rehabilitation. In general, the contour information can elaborately describe the shape and size of lesions in a med... Read More about Contour extraction of medical images using an attention-based network..

LDDNet: a deep learning framework for the diagnosis of infectious lung diseases. (2023)
Journal Article
PODDER, P., RANI DAS, S., MONDAL, M.R.H., BHARATI, S., MALIHA, A., HASAN, M.J. and PILTAN, F. 2023. LDDNet: a deep learning framework for the diagnosis of infectious lung diseases. Sensors [online], 23(1), article 480. Available from: https://doi.org/10.3390/s23010480

This paper proposes a new deep learning (DL) framework for the analysis of lung diseases, including COVID-19 and pneumonia, from chest CT scans and X-ray (CXR) images. This framework is termed optimized DenseNet201 for lung diseases (LDDNet). The pro... Read More about LDDNet: a deep learning framework for the diagnosis of infectious lung diseases..

Automated analysis of sleep study parameters using signal processing and artificial intelligence. (2022)
Journal Article
SOHAIB, M., GHAFFAR, A., SHIN, J., HASAN. M.J. and SULEMAN, M.T. 2022. Automated analysis of sleep study parameters using signal processing and artificial intelligence. International journal of environmental research and public health [online], 19(20), article number 13256. Available from: https://doi.org/10.3390/ijerph192013256

An automated sleep stage categorization can readily face noise-contaminated EEG recordings, just as other signal processing applications. Therefore, the denoising of the contaminated signals is inevitable to ensure a reliable analysis of the EEG sign... Read More about Automated analysis of sleep study parameters using signal processing and artificial intelligence..

TransSLC: skin lesion classification in dermatoscopic images using transformers. (2022)
Conference Proceeding
SARKER, M.M.K., MORENO-GARCÍA, C.F., REN, J. and ELYAN, E. 2022. TransSLC: skin lesion classification in dermatoscopic images using transformers. In Yang, G., Aviles-Rivero, A., Roberts, M. and Schönlieb, C.-B. (eds.) Medical image understanding and analysis: proceedings of 26th Medical image understanding and analysis 2022 (MIUA 2022), 27-29 July 2022, Cambridge, UK. Lecture notes in computer sciences, 13413. Cham: Springer [online], pages 651-660. Available from: https://doi.org/10.1007/978-3-031-12053-4_48

Early diagnosis and treatment of skin cancer can reduce patients' fatality rates significantly. In the area of computer-aided diagnosis (CAD), the Convolutional Neural Network (CNN) has been widely used for image classification, segmentation, and rec... Read More about TransSLC: skin lesion classification in dermatoscopic images using transformers..

Ensemble of deep learning models with surrogate-based optimization for medical image segmentation. (2022)
Conference Proceeding
DANG, T., LUONG, A.V., LIEW, A.W.C., MCCALL, J. and NGUYEN, T.T. 2022. Ensemble of deep learning models with surrogate-based optimization for medical image segmentation. In 2022 IEEE (Institute of Electrical and Electronics Engineers) Congress on evolutionary computation (CEC 2022), co-located with 2022 IEEE International joint conferences on neural networks (IJCNN 2022), 2022 IEEE International conference on fuzzy systems (FUZZ-IEEE 2022), 18-23 July 2022, Padua, Italy. Piscataway: IEEE (online), article #1030. Available from: https://doi.org/10.1109/CEC55065.2022.9870389

Deep Neural Networks (DNNs) have created a breakthrough in medical image analysis in recent years. Because clinical applications of automated medical analysis are required to be reliable, robust and accurate, it is necessary to devise effective DNNs... Read More about Ensemble of deep learning models with surrogate-based optimization for medical image segmentation..

Computer vision and machine learning for medical image analysis: recent advances, challenges, and way forward. (2022)
Journal Article
ELYAN, E., VUTTIPITTAYAMONGKOL, P., JOHNSTON, P., MARTIN, K., MCPHERSON, K., MORENO-GARCIA, C.F., JAYNE, C. and SARKER, M.M.K. 2022. Computer vision and machine learning for medical image analysis: recent advances, challenges, and way forward. Artificial intelligence surgery [online], 2, pages 24-25. Available from: https://doi.org/10.20517/ais.2021.15

The recent development in the areas of deep learning and deep convolutional neural networks has significantly progressed and advanced the field of computer vision (CV) and image analysis and understanding. Complex tasks such as classifying and segmen... Read More about Computer vision and machine learning for medical image analysis: recent advances, challenges, and way forward..

Effective extraction of ventricles and myocardium objects from cardiac magnetic resonance images with a multi-task learning U-net. (2021)
Journal Article
REN, J., SUN, H., ZHAO, H., GAO, H., MACLELLAN, C., ZHAO, S. and LUO, X. 2022. Effective extraction of ventricles and myocardium objects from cardiac magnetic resonance images with a multi-task learning U-net. Pattern recognition letters [online], 155, pages 165-170. Available from: https://doi.org/10.1016/j.patrec.2021.10.025

Accurate extraction of semantic objects such as ventricles and myocardium from magnetic resonance (MR) images is one essential but very challenging task for the diagnosis of the cardiac diseases. To tackle this problem, in this paper, an automatic en... Read More about Effective extraction of ventricles and myocardium objects from cardiac magnetic resonance images with a multi-task learning U-net..

A means of assessing deep learning-based detection of ICOS protein expression in colon cancer. (2021)
Journal Article
SARKER, M.M.K., MAKHLOUF, Y., CRAIG, S.G., HUMPHRIES, M.P., LOUGHREY, M., JAMES, J.A., SALTO-TELLEZ, M., O'REILLY, P. and MAXWELL, P. 2021. A means of assessing deep learning-based detection of ICOS protein expression in colon cancer. Cancers [online], 13(15): machine learning techniques in cancer, article 3825. Available from: https://doi.org/10.3390/cancers13153825

Biomarkers identify patient response to therapy. The potential immune‐checkpoint bi-omarker, Inducible T‐cell COStimulator (ICOS), expressed on regulating T‐cell activation and involved in adaptive immune responses, is of great interest. We have prev... Read More about A means of assessing deep learning-based detection of ICOS protein expression in colon cancer..

A novel multi-stage residual feature fusion network for detection of COVID-19 in chest X-ray images. (2021)
Journal Article
FANG, Z., REN, J., MACLELLAN, C., LI, H., ZHOA, H., HUSSAIN, A. and FORTINO, G. 2022. A novel multi-stage residual feature fusion network for detection of COVID-19 in chest X-ray images. IEEE transactions on molecular, biological and multi-scale communications [online], 8(1), pages 17-27. Available from: https://doi.org/10.1109/tmbmc.2021.3099367

To suppress the spread of COVID-19, accurate diagnosis at an early stage is crucial, chest screening with radiography imaging plays an important role in addition to the real-time reverse transcriptase polymerase chain reaction (RT-PCR) swab test. Due... Read More about A novel multi-stage residual feature fusion network for detection of COVID-19 in chest X-ray images..

Weighted ensemble of deep learning models based on comprehensive learning particle swarm optimization for medical image segmentation. (2021)
Conference Proceeding
DANG, T., NGUYEN, T.T., MORENO-GARCIA, C.F., ELYAN, E. and MCCALL, J. 2021. Weighted ensemble of deep learning models based on comprehensive learning particle swarm optimization for medical image segmentation. In Proceeding of 2021 IEEE (Institute of electrical and electronics engineers) Congress on evolutionary computation (CEC 2021), 28 June - 1 July 2021, Kraków, Poland : [virtual conference]. Piscataway: IEEE [online], pages 744-751. Available from: https://doi.org/10.1109/CEC45853.2021.9504929

In recent years, deep learning has rapidly become a method of choice for segmentation of medical images. Deep neural architectures such as UNet and FPN have achieved high performances on many medical datasets. However, medical image analysis algorith... Read More about Weighted ensemble of deep learning models based on comprehensive learning particle swarm optimization for medical image segmentation..

Rotate vector (Rv) reducer fault detection and diagnosis system: towards component level prognostics and health management (phm). (2020)
Journal Article
ROHAN, A., RAOUF, I. and KIM, H.S. 2020. Rotate vector (Rv) reducer fault detection and diagnosis system: towards component level prognostics and health management (phm). Sensors [online], 20(23), article 6845. Available from: https://doi.org/10.3390/s20236845

In prognostics and health management (PHM), the majority of fault detection and diagnosis is performed by adopting segregated methodology, where electrical faults are detected using motor current signature analysis (MCSA), while mechanical faults are... Read More about Rotate vector (Rv) reducer fault detection and diagnosis system: towards component level prognostics and health management (phm)..

Sleep state classification using power spectral density and residual neural network with multichannel EEG signals. (2020)
Journal Article
HASAN, M.J., SHON, D., IM, K., CHOI, H.-K., YOO, D.-S. and KIM, J.-M. 2020. Sleep state classification using power spectral density and residual neural network with multichannel EEG signals. Applied sciences [online], 10(21): medical signal and image processing, article 7639. Available from: https://doi.org/10.3390/app10217639

This paper proposes a classification framework for automatic sleep stage detection in both male and female human subjects by analyzing the electroencephalogram (EEG) data of polysomnography (PSG) recorded for three regions of the human brain, i.e., t... Read More about Sleep state classification using power spectral density and residual neural network with multichannel EEG signals..

Web‐based efficient dual attention networks to detect COVID‐19 from X‐ray images. (2020)
Journal Article
SARKER, M.M.K., MAKHLOUF, Y., BANU, S.F., CHAMBON, S., RADEVA, P. and PUIG, D. 2020. Web-based efficient dual attention networks to detect COVID-19 from X-ray images. Electronics letters [online], 56(24), pages 1298-1301. Available from: https://doi.org/10.1049/el.2020.1962

Rapid and accurate detection of COVID-19 is a crucial step to control the virus. For this purpose, the authors designed a web-based COVID-19 detector using efficient dual attention networks, called ‘EDANet’. The EDANet architecture is based on invert... Read More about Web‐based efficient dual attention networks to detect COVID‐19 from X‐ray images..

Effective melanoma recognition using deep convolutional neural network with covariance discriminant loss. (2020)
Journal Article
GUO, L., XIE, G., XU, X. and REN, J. 2020. Effective melanoma recognition using deep convolutional neural network with covariance discriminant loss. Sensors [online], 20(20), article 5786. Available from: https://doi.org/10.3390/s20205786

Melanoma recognition is challenging due to data imbalance and high intra-class variations and large inter-class similarity. Aiming at the issues, we propose a melanoma recognition method using deep convolutional neural network with covariance discrim... Read More about Effective melanoma recognition using deep convolutional neural network with covariance discriminant loss..

Human pose estimation-based real-time gait analysis using convolutional neural network. (2020)
Journal Article
ROHAN, A., RABAH, M., HOSNY, T. and KIM, S.-H. 2020. Human pose estimation-based real-time gait analysis using convolutional neural network. IEEE access [online] 8, pages 191542-191550. Available from: https://doi.org/10.1109/ACCESS.2020.3030086

Gait analysis is widely used in clinical practice to help in understanding the gait abnormalities and its association with a certain underlying medical condition for better diagnosis and prognosis. Several technologies embedded in the specialized dev... Read More about Human pose estimation-based real-time gait analysis using convolutional neural network..

A novel intelligent computational approach to model epidemiological trends and assess the impact of non-pharmacological interventions for COVID-19. (2020)
Journal Article
REN, J., YAN, Y., ZHAO, H., MA, P., ZABALZA, J., HUSSAIN, Z., LUO, S., DAI, Q., ZHAO, S., SHEIKH, A., HUSSAIN, A. and LI, H. 2020. A novel intelligent computational approach to model epidemiological trends and assess the impact of non-pharmacological interventions for COVID-19. IEEE Journal of biomedical and health informatics [online], 24(12), pages 3551-3563. Available from: https://doi.org/10.1109/jbhi.2020.3027987

The novel coronavirus disease 2019 (COVID-19) pandemic has led to a worldwide crisis in public health. It is crucial we understand the epidemiological trends and impact of non-pharmacological interventions (NPIs), such as lockdowns for effective mana... Read More about A novel intelligent computational approach to model epidemiological trends and assess the impact of non-pharmacological interventions for COVID-19..

A hybrid feature pool-based emotional stress state detection algorithm using EEG signals. (2019)
Journal Article
HASAN, M.J. and KIM, J.-M. 2019. A hybrid feature pool-based emotional stress state detection algorithm using EEG signals. Brain sciences [online], 9(12), article number 376. Available from: https://doi.org/10.3390/brainsci9120376

Human stress analysis using electroencephalogram (EEG) signals requires a detailed and domain‐specific information pool to develop an effective machine learning model. In this study, a multi‐domain hybrid feature pool is designed to identify most of... Read More about A hybrid feature pool-based emotional stress state detection algorithm using EEG signals..

Breast tumor segmentation and shape classification in mammograms using generative adversarial and convolutional neural network. (2019)
Journal Article
SINGH, V.K., RASHWAN, H.A., ROMANI, S., AKRAM, F., PANDEY, N., SARKER, M.M.K., SALEH, A., ARENAS, M., ARQUEZ, M., PUIG, D. and TORRENTS-BARRENA, J. 2020. Breast tumor segmentation and shape classification in mammograms using generative adversarial and convolutional neural network. Expert systems with applications [online], 139, article number 112855. Available from: https://doi.org/10.1016/j.eswa.2019.112855

Mammogram inspection in search of breast tumors is a tough assignment that radiologists must carry out frequently. Therefore, image analysis methods are needed for the detection and delineation of breast tumors, which portray crucial morphological in... Read More about Breast tumor segmentation and shape classification in mammograms using generative adversarial and convolutional neural network..

Conditional generative adversarial and convolutional networks for X-ray breast mass segmentation and shape classification. (2018)
Conference Proceeding
SINGH, V.K., ROMANI, S., RASHWAN, H.A., AKRAM, F., PANDEY, N., SARKER, M.M.K., ABDULWAHAB, S., TORRENTS-BARRENA, J., SALEH, A., ARQUEZ, M., ARENAS, M. and PUIG, D. 2018. Conditional generative adversarial and convolutional networks for X-ray breast mass segmentation and shape classification. In Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-López, C. and Fictinger, G. (eds.) Medical image computing and computer assisted intervention (MICCAI 2018): proceedings of 21st international conference on Medical image computing and computer assisted interventions 2018 (MICCAI 2018), 16-20 September 2018, Granada, Spain. Lecture notes in computer science, 11071. Cham: Springer [online], pages 833-840. Available from: https://doi.org/10.1007/978-3-030-00934-2_92

This paper proposes a novel approach based on conditional Generative Adversarial Networks (cGAN) for breast mass segmentation in mammography. We hypothesized that the cGAN structure is well-suited to accurately outline the mass area, especially when... Read More about Conditional generative adversarial and convolutional networks for X-ray breast mass segmentation and shape classification..