All Outputs (16)

Efficient breast cancer classification network with dual squeeze and excitation in histopathological images. (2022)
Journal Article
SARKER, M.M.K., AKRAM, F., ALSHARID, M., SINGH, V.K., YASRAB, R. and ELYAN, E. 2023. Efficient breast cancer classification network with dual squeeze and excitation in histopathological images. Diagnostics [online], 13(1), article 103. Available from: https://doi.org/10.3390/diagnostics13010103

Medical image analysis methods for mammograms, ultrasound, and magnetic resonance imaging (MRI) cannot provide the underline features on the cellular level to understand the cancer microenvironment which makes them unsuitable for breast cancer subtyp... Read More about Efficient breast cancer classification network with dual squeeze and excitation in histopathological images..

ICOSeg: real-time ICOS protein expression segmentation from immunohistochemistry slides using a lightweight conv-transformer network. (2022)
Journal Article
SINGH, V.K., SARKER, M.M.K., MAKHLOUF, Y., CRAIG, S.G., HUMPHRIES, M.P., LOUGHREY, M.B., JAMES, J.A., SALTO-TELLEZ, M., O'REILLY, P. and MAXWELL, P. 2022. ICOSeg: real-time ICOS protein expression segmentation from immunohistochemistry slides using a lightweight conv-transformer network. Cancers [online], 14(16), article 3910. Available from: https://doi.org/10.3390/cancers14163910

In this article, we propose ICOSeg, a lightweight deep learning model that accurately segments the immune-checkpoint biomarker, Inducible T-cell COStimulator (ICOS) protein in colon cancer from immunohistochemistry (IHC) slide patches. The proposed m... Read More about ICOSeg: real-time ICOS protein expression segmentation from immunohistochemistry slides using a lightweight conv-transformer network..

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..

AWEU-Net: an attention-aware weight excitation U-Net for lung nodule segmentation. (2021)
Journal Article
BANU, S.F., SARKER, M.M.K., ABDEL-NASSER, M., PUIG, D. and RASWAN, H.A. 2021. AWEU-Net: an attention-aware weight excitation U-Net for lung nodule segmentation. Applied science [online], 11(21), article 10132. Available from: https://doi.org/10.3390/app112110132

Lung cancer is a deadly cancer that causes millions of deaths every year around the world. Accurate lung nodule detection and segmentation in computed tomography (CT) images is a vital step for diagnosing lung cancer early. Most existing systems face... Read More about AWEU-Net: an attention-aware weight excitation U-Net for lung nodule segmentation..

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..

SLSNet: skin lesion segmentation using a lightweight generative adversarial network. (2021)
Journal Article
SARKER, M.M.K., RASHWAN, H.A., AKRAM, F., SINGH, V.K., BANU, S.F., CHOWDHURY, F.U.H., CHOUDHURY, K.A., CHAMBON, S., RADEVA, P., PUIG, D. and ABDEL-NASSER, M. 2021. SLSNet: skin lesion segmentation using a lightweight generative adversarial network. Expert systems with applications [online], 183, article 115433. Available from: https://doi.org/10.1016/j.eswa.2021.115433

The determination of precise skin lesion boundaries in dermoscopic images using automated methods faces many challenges, most importantly, the presence of hair, inconspicuous lesion edges and low contrast in dermoscopic images, and variability in the... Read More about SLSNet: skin lesion segmentation using a lightweight generative adversarial network..

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..

Breast tumor segmentation in ultrasound images using contextual-information-aware deep adversarial learning framework. (2020)
Journal Article
SINGH, V.K., ABDEL-NASSER, M., AKRAM, F., RASHWAN, H.A., SARKER, M.M.K., PANDEY, N., ROMANI, S. and PUIG, D. 2020. Breast tumor segmentation in ultrasound images using contextual-information-aware deep adversarial learning framework. Expert systems with applications [online], 162, article 113870. Available from: https://doi.org/10.1016/j.eswa.2020.113870

Automatic tumor segmentation in breast ultrasound (BUS) images is still a challenging task because of many sources of uncertainty, such as speckle noise, very low signal-to-noise ratio, shadows that make the anatomical boundaries of tumors ambiguous,... Read More about Breast tumor segmentation in ultrasound images using contextual-information-aware deep adversarial learning framework..

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..

Hierarchical approach to classify food scenes in egocentric photo-streams. (2019)
Journal Article
MARTINEZ, E.T., LEYVA-VALLINA, M., SARKER, M.M.K., PUIG, D., PETKOV, N. and RADEVA, P. 2020. Hierarchical approach to classify food scenes in egocentric photo-streams. IEEE journal of biomedical and health informatics [online], 24(3), pages 866-877. Available from: https://doi.org/10.1109/JBHI.2019.2922390

Recent studies have shown that the environment where people eat can affect their nutritional behavior. In this paper, we provide automatic tools for personalized analysis of a person's health habits by the examination of daily recorded egocentric pho... Read More about Hierarchical approach to classify food scenes in egocentric photo-streams..

Recognizing food places in egocentric photo-streams using multi-scale atrous convolutional networks and self-attention mechanism. (2019)
Journal Article
SARKER, M.M.K., RASHWAN, H.A., AKRAM, F., TALAVERA, E., BANU, S.F., RADEVA, P. and PUIG, D. 2019. Recognizing food places in egocentric photo-streams using multi-scale atrous convolutional networks and self-attention mechanism. IEEE access [online], 7, pages 39069-39082. Available from: https://doi.org/10.1109/ACCESS.2019.2902225

Wearable sensors (e.g., lifelogging cameras) represent very useful tools to monitor people's daily habits and lifestyle. Wearable cameras are able to continuously capture different moments of the day of their wearers, their environment, and interacti... Read More about Recognizing food places in egocentric photo-streams using multi-scale atrous convolutional networks and self-attention mechanism..

Segmentation and recognition of Korean vehicle license plate characters based on the global threshold method and the cross-correlation matching algorithm. (2016)
Journal Article
SARKER, M.M.K. and SONG, M.K. 2016. Segmentation and recognition of Korean vehicle license plate characters based on the global threshold method and the cross-correlation matching algorithm. Journal of information processing systems [online], 12(4), pages 661-680. Available from: https://doi.org/10.3745/JIPS.02.0050

The vehicle license plate recognition (VLPR) system analyzes and monitors the speed of vehicles, theft of vehicles, the violation of traffic rules, illegal parking, etc., on the motorway. The VLPR consists of three major parts: license plate detectio... Read More about Segmentation and recognition of Korean vehicle license plate characters based on the global threshold method and the cross-correlation matching algorithm..

Detection and recognition of illegally parked vehicles based on an adaptive gaussian mixture model and a seed fill algorithm. (2015)
Journal Article
SARKER, M.M.K., WEIHUA, C. and SONG, M.K. 2015. Detection and recognition of illegally parked vehicles based on an adaptive gaussian mixture model and a seed fill algorithm. Journal of information and communication convergence engineering [online], 13(3), pages 197-204. Available from: https://doi.org/10.6109/jicce.2015.13.3.197

In this paper, we present an algorithm for the detection of illegally parked vehicles based on a combination of some image processing algorithms. A digital camera is fixed in the illegal parking region to capture the video frames. An adaptive Gaussia... Read More about Detection and recognition of illegally parked vehicles based on an adaptive gaussian mixture model and a seed fill algorithm..

Real-time vehicle license plate detection based on background subtraction and cascade of boosted classifiers. (2014)
Journal Article
SARKER, M.M. and SONG, M.K. 2014. Real-time vehicle license plate detection based on background subtraction and cascade of boosted classifiers. Journal of Korean Institute of Communications and Information Sciences [online], 39C(10), pages 909-919. Available from: https://doi.org/10.7840/kics.2014.39c.10.909

License plate (LP) detection is the most imperative part of an automatic LP recognition (LPR) system. Typical LPR contains two steps, namely LP detection (LPD) and character recognition. In this paper, we propose an efficient Vehicle-to-LP detection... Read More about Real-time vehicle license plate detection based on background subtraction and cascade of boosted classifiers..

A fast and robust license plate detection algorithm based on two-stage cascade AdaBoost. (2014)
Journal Article
SARKER, M.M.K., YOON, S. and PARK, D.S. 2014. A fast and robust license plate detection algorithm based on two-stage cascade AdaBoost. KSII transactions on internet and information systems [online], 8(10), pages 3490-3507. Available from: https://doi.org/10.3837/tiis.2014.10.012

License plate detection (LPD) is one of the most important aspects of an automatic license plate recognition system. Although there have been some successful license plate recognition (LPR) methods in past decades, it is still a challenging problem b... Read More about A fast and robust license plate detection algorithm based on two-stage cascade AdaBoost..

Modeling and implementing two-stage AdaBoost for real-time vehicle license plate detection. (2014)
Journal Article
SONG, M.K. and SARKER, M.M.K. 2014. Modeling and implementing two-stage AdaBoost for real-time vehicle license plate detection. Journal of applied mathematics [online], 2014: advanced mathematics and numerical modeling of IoT (Internet of Things), article ID 697358. Available from: https://doi.org/10.1155/2014/697658

License plate (LP) detection is the most imperative part of the automatic LP recognition system. In previous years, different methods, techniques, and algorithms have been developed for LP detection (LPD) systems. This paper proposes to automatical d... Read More about Modeling and implementing two-stage AdaBoost for real-time vehicle license plate detection..